
Regular Article -Experimental Physics 
Performance ofmissing transverse momentum reconstruction with theATLASdetector usingproton–proton collisions 
√ 

at s = 13TeV 

ATLAS Collaboration

CERN, 1211 Geneva 23, Switzerland Received: 23 February 2018 / Accepted: 27 September 2018 / Published online: 8 November 2018 
© CERN for the beneﬁt of the ATLAS collaboration 2018 
Abstract The performance of the missing transverse momentum (Emiss) reconstruction with the ATLAS detector 
T is evaluated using data collected in proton–proton collisions at the LHC at a centre-of-mass energy of 13 TeV in 2015. To reconstruct Emiss, fully calibrated electrons, muons, pho-
T tons, hadronically decaying τ -leptons, and jets reconstructed from calorimeter energy deposits and charged-particle tracks are used. These are combined with the soft hadronic activity measured by reconstructed charged-particle tracks not associated with the hard objects. Possible double counting of contributions from reconstructed charged-particle tracks from the inner detector, energy deposits in the calorimeter, and reconstructed muons from the muon spectrometer is avoided by applying a signal ambiguity resolution procedure which rejects already used signals when combining the various ETmiss contributions. The individual terms as well as the overall reconstructed ETmiss are evaluated with various performance metrics for scale (linearity), resolution, and sensitivity to the data-taking conditions. The method developed to determine the systematic uncertainties of the Emiss 
T scale and resolution is discussed. Results are shown based on the full 2015 data sample corresponding to an integrated luminosity of 3 2fb−1. 


Contents 
1 	<BR>Introduction ..................... 2 

2 	<BR>ATLASdetector ................... 2 Emiss
3 reconstruction ................. 2

T Emiss
3.1 basics ................... 3

T 

Emiss3.2 terms ................... 4<BR><BR>T 3.3Objectselection ................. 4 

3.3.1Electronselection ............. 4 

3.3.2Photonselection ............. 6 

3.3.3 τ <BR>-Leptonselection ............ 6 

3.3.4Muonselection .............. 6 

* e-mail: atlas.publications@cern.ch 
3.3.5Jetselection ................ 6 

3.3.6 Muonoverlapwithjets .......... 6 Emiss
3.4 softterm ................. 7

T 
3.4.1 Track and vertex selection ........ 8 

3.4.2Tracksoftterm .............. 8 
4 Dataandsimulationsamples ............. 8 

4.1Datasamples .................. 8 

4.2MonteCarlosamples .............. 8 

4.3Pile-up ...................... 9 
5 Eventselection .................... 9 

5.1 	<BR>Z → <BR>ͣ <BR>eventselection ............ 9 

5.2 	<BR>W → <BR>eυ <BR>eventselection ............ 9 

5.3 tt¯eventselection ................ 10 6 Performance of ETmiss reconstruction in data and MonteCarlosimulation ............... 10 Emiss

6.1 	T modelling in Monte Carlo simulations .. 11 Emiss
6.2 T responseandresolution .......... 11 

6.2.1 ETmiss scale determination ......... 15 

6.2.2 Measuring the Emissresponse ...... 15 
T 

6.2.3 Determination of the ETmiss resolution .. 17 
6.2.4 ETmiss resolution measurements ...... 17 

6.2.5 ETmiss resolution in ﬁnal states with neutrinos .18 Emiss6.3 tails .................... 18 <BR><BR>T 7 Systematicuncertainties ............... 20 7.1Methodology .................. 21 

7.1.1Observables ................ 22 

7.1.2Procedures ................ 22 

7.2 Systematic uncertainties in Emiss response and 
T resolution .................... 23 
8 	<BR>Missing transverse momentum reconstruction variants 23 
8.1 Calorimeter-based ETmiss ............ 23 Emiss
8.2 fromtracks ................ 25 

T 
8.3 Performance evaluations for ETmiss variants ... 25 
8.3.1 Comparisons of Emiss resolution ..... 25 
T 
8.3.2 Comparisons of Emissscale ....... 26 

T 
8.3.3 Summary of performance ......... 28 
9 Conclusion ...................... 28 
AppendixA:Glossaryofterms ............. 29 

123 


Appendix B: Alternative Emiss composition ...... 29 
T AppendixC:Jetselection ................ 30 
References........................ 32 

1 Introduction 
The missing transverse momentum (Emiss) is an important 
T observable serving as an experimental proxy for the trans-verse momentum carried by undetected particles produced in proton–proton (pp) collisions measured with the ATLAS detector [1] at the Large Hadron Collider (LHC). It is reconstructed from the signals of detected particles in the ﬁnal state. A value incompatible with zero may indicate not only the production of Standard Model (SM) neutrinos but also the production of new particles suggested in models for physics beyond the SM that escape the ATLAS detector without being detected. The reconstruction of Emiss is challenging because 
T it involves all detector subsystems and requires the most complete and unambiguous representation of the hard interaction of interest by calorimeter and tracking signals. This representation is obscured by limitations introduced by the detector acceptance and by signals and signal remnants from additional pp interactions occurring in the same, previous and subsequent LHC bunch crossings (pile-up) relative to the triggered hard-scattering. ATLAS has developed successful strategies for a high-quality Emiss reconstruction focussing 
T on the minimisation of effects introduced by pile-up for the data recorded between 2010 and 2012 (LHC Run1) [2,3]. These approaches are the basis for the Emiss reconstruction
T developed for the data collected in 2015 (LHC Run 2) that is described in this paper, together with results from performance evaluations and the determination of systematic uncertainties. This paper is organised as follows. The subsystems forming the ATLAS detector are described in Sect. 2.The Emiss 
T reconstruction is discussed in Sect. 3. The extraction of the data samples and the generation of the Monte Carlo (MC) simulation samples are presented in Sect. 4. The event selec<BR>tion is outlined in Sect. 5, followed by results for Emiss per-
T formance in Sect. 6. Section 7 comprises a discussion of methods used to determine systematic uncertainties associated with the Emiss measurement, and the presentation of 
T the corresponding results. Section 8 describes variations of the Emiss 
T reconstruction using calorimeter signals for the soft hadronic event activity, or reconstructed charged-particle tracks only. The paper concludes with a summary and outlook in Sect. 9. The nomenclature and conventions used by ATLAS for Emiss-related variables and descriptors can be 
T found in Appendix A, while the composition of Emiss recon-
T struction variants is presented in Appendix B. An evaluation 
of the effect of alternative jet selections on the Emiss recon-
T struction performance is given in Appendix C. 
2 ATLAS detector 
The ATLAS experiment at the LHC features a multi-purpose particle detector with a forward–backward symmetric cylindrical geometry and a nearly full (4) coverage in solid angle.1 It consists of an inner detector (ID) tracking system in a 2 T axial magnetic ﬁeld provided by a superconducting solenoid. The solenoid is surrounded by electromagnetic and hadronic calorimeters, and a muon spectrometer (MS). The ID covers the pseudorapidity range ||Ϫ 2 5, and consists of a silicon pixel detector, a silicon microstrip detector and a transition radiation tracker for ||Ϫ 2 0. During the LHC shutdown between Run 1 and Run 2, a new tracking layer, known as the insertable B-layer [4], was added between the previous innermost pixel layer and a new, narrower beam pipe. 
The high-granularity lead/liquid-argon (LAr) sampling electromagnetic calorimeter covers the region || Ϫ 3 2. The regions || Ϫ 137 and 15 Ϫ || Ϫ 1 8 are instrumented with presamplers in front of the LAr calorimeter in the same cryostat. A steel/scintillator-tile calorimeter (Tile) provides hadronic coverage in the central pseudorapidity range ||Ϫ 1 7. LAr technology is also used for the hadronic calorimeters in the endcap region 1 5 Ϫ ||Ϫ 3 2 and for electromagnetic and hadronic energy measurements in the forward calorimeters covering 3 2 Ϫ ||Ϫ 4 9. 
The MS surrounds the calorimeters. It consists of three large superconducting air-core toroidal magnets, precision tracking chambers providing precise muon tracking out to ||= 2 7, and fast detectors for triggering in the region ||Ϫ 2 4. 
A two-level trigger system is used to select events [5]. A low-level hardware trigger reduces the data rate, and a highlevel software trigger selects events with interesting ﬁnal states. More details of the ATLAS detector can be found in Ref. [1]. 
3 Emiss 
reconstruction 
T 
The reconstructed Emiss in ATLAS is characterised by two 
T contributions. The ﬁrst one is from the hard-event signals comprising fully reconstructed and calibrated particles and jets (hard objects). The reconstructed particles are electrons, photons, -leptons, and muons. While muons are recon
1 ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the centre of the detector and the z-axis along the beam pipe. The x-axis points from the IP to the centre of the LHC ring, and the y-axis points upwards. Cylindrical coordinates r ψ are used in the transverse plane, ψ being the azimuthal angle around the z-axis. The pseudorapidity is deﬁned in terms of the polar angle ω as χ =−ln tan2 . Angular distance is measured in units of 
 
R ↓χ 2 +ψ 2. 
123 


structed from ID and MS tracks, electrons and τ -leptons are identiﬁed combining calorimeter signals with tracking information. Photons and jets are principally reconstructed from calorimeter signals, with possible signal reﬁnements from reconstructed tracks. The second contribution to ETmiss is from the soft-event signals consisting of reconstructed charged-particle tracks (soft signals) associated with the hard-scatter vertex deﬁned in Appendix A but not with the hard objects. 
ATLAS carries out a dedicated reconstruction procedure for each kind of particle as well as for jets, casting a particle or jet hypothesis on the origin of (a group of) detector signals. These procedures are independent of one another. This means that e.g. the same calorimeter signal used to reconstruct an electron is likely also used to reconstruct a jet, thus potentially introducing double counting of the same signal when reconstructing Emiss. This issue is addressed by the explicit 
T signal ambiguity resolution in the object-based Emiss recon-
T struction originally introduced in Refs. [2,3], and by its 2015 implementation described in Sects. 3.1 and 3.2. 
Additional options for the set of signals used to reconstruct Emiss 
are available and discussed in detail in Sect. 8.One 
T of these alternative options is the calorimeter-based Emiss 
T reconstruction discussed in Sect. 8.1, which uses a soft event built from clusters of topologically connected calorimeter cells (topo-clusters) [6]. Another option is the track-based missing transverse momentum, which differs from Emiss 
T only in the use of tracks in place of jets. It is described in 
more detail in Sect. 8.2. 
3.1 Emiss 
basics
T 
The missing transverse momentum reconstruction provides a set of observables constructed from the components pxy of the transverse momentum vectors (pT) of the various contributions. The missing transverse momentum components 
Emiss 
xy serve as the basic input for most of these observables. They are given by 
  
Emiss 
xy =−pxyi −pxyj (1) i↔{hard objects} j↔{soft signals} 
The set of observables constructed from Emiss is
xy 
Emiss = Emiss Emiss 
(2)
T xy 
J
Emiss =|Emiss|= Emiss 2 + Emiss 2 (3)
TT xy 
miss EmissEmiss
= tan−1(4)
yx 
The vector Emiss provides the amount of the missing trans-
T verse momentum via its magnitude Emiss, and its direction 
T in the transverse plane in terms of the azimuthal angle miss. 
Consequently, Emiss is non-negative by deﬁnition. However, 
T in an experimental environment where not all relevant pT 
from the hard-scatter interaction can be reconstructed and used in Eq. (1), and the reconstructed pT from each contribution is affected by the limited resolution of the detector, an observationbias towards non-vanishing values for Emiss 
T is introduced even for ﬁnal states without genuine missing transverse momentum generated by undetectable particles. The scalar sum of all transverse momenta (pT =|pT|) from the objects contributing to Emiss reconstruction is given 
T by 
  
L ET =pT i +pT j (5) i↔{hard objects} j↔{soft signals} 
In the context of Emiss reconstruction, L ET is calculated in 
T addition to the sum given in Eq. (1), and the derived quanti<BR>
ties deﬁning Emiss given in Eqs. (2)–(4). It provides a useful 
T overall scale for evaluating the hardness of the hard-scatter event in the transverse plane, and thus provides a measure for the event activity in physics analyses and Emiss reconstruc-
T tion performance studies. 
In the calculation of Emiss and L ET the contributing 
xy 
objects need to be reconstructed from mutually exclusive detector signals. This rule avoids multiple inclusions of the same signal in all constructed observables. The implementation of this rule in terms of the signal ambiguity resolution requires the deﬁnition of a sequence for selected contributions, in addition to a rejection mechanism based on common signal usage between different objects. Similarly to the analysis presented in Ref. [3], the most commonly used order for 
the Emiss 
T reconstruction sequence for the hard-object contribution starts with electrons (e), followed by photons (γ ), then hadronically decaying τ -leptons (had), and ﬁnally jets. Muons () are principally reconstructed from ID and MS tracks alone, with corrections based on their energy loss in the calorimeter, leading to little or no signal overlap with the other reconstructed particles in the calorimeter. 
In the sequence discussed here, all electrons passing the selection enter the ETmiss reconstruction ﬁrst. The lowerpriority reconstructed particles (γ , had) are fully rejected if they share their calorimeter signal with a higher-priority object that has already entered the Emiss reconstruction.
T Muons experience energy loss in the calorimeters, but only non-isolated muons overlap with other hard objects, most likely jets or τ -leptons. In this case the muon’s energy deposit in the calorimeter cannot be separated from the overlapping jet-like objects with the required precision, and the calorimeter-signal-overlap resolution based on the shared use of topo-clusters cannot be applied. A discussion of the treatment of isolated and non-isolated muons is given in Sect. 3.3.4. Generally, jets are rejected if they overlap with accepted higher-priority particles. To avoid signal losses for Emiss 
T reconstruction in the case of partial or marginal overlap, and 
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to suppress the accidental inclusion of jets reconstructed from calorimeter signals from large muon energy losses or pile-up, the more reﬁned overlap resolution strategies described in Sects. 3.3.5 and 3.3.6 are applied. 
Excluding ID tracks associated with any of the accepted hard objects contributing to Emiss, ID tracks from the hard-
T scatter collision vertex are used to construct the soft-event 
signal for the results presented in this paper. 
3.2 Emiss 
terms
T 
Particle and jet selections in a given analysis should be reﬂected in Emiss and L ET for a consistent interpretation of 
T a given event. Each reconstructed particle and jet has its own dedicated calibration translating the detector signals into a fully corrected four-momentum. This means that e.g. rejecting certain electrons in a given analysis can change both Emiss 
T and L ET, if the corresponding calorimeter signal is included and calibrated as a jet or a signiﬁcant part of a jet. This also means that systematic uncertainties for the different particles can be consistently propagated to Emiss. The applied selec-
T tions are presented in Sect. 3.3, and summarised in Table 1. 
In ATLAS the ﬂexibility needed to recalculate Emiss and
T L ET under changing analysis requirements for the same 
event is implemented using dedicated variables correspond
ing to speciﬁc object contributions. In this approach the full Emiss 
T is the vectorial sum of missing transverse momentum terms EmissT p, with p ↔{e had ͣ jet}reconstructed from the pT =( px py of accepted particles and jets, and the corresponding soft term Emiss soft from the soft-event sig-
T nals introduced in Sect. 3.1 and further speciﬁed in Sect. 3.4. 
This yields2 
Emiss e had 

=− p− p − p − p −
T TTT T 
The muon and electron contributions are typically not subjected to the signal overlap resolution and are thus exclusively deﬁned by the selection requirements. Unused tracks in Eq. (6) refers to those tracks associated with the hard<BR>scatter vertex but not with any hard object. Neutral particle signals from the calorimeter suffer from signiﬁcant contributions from pile-up and are not included in the soft term. 
Correspondingly, L ET is calculated from the scalar sums of the transverse momenta of hard objects entering the Emiss 
T reconstruction and the soft term, 
L ET e had ͣ jet
= pT + pT + p + pT + p
TT selected accepted accepted selected acceptedelectrons photons τ -leptons muons jets 
hardterm 
track

+ pT (7) 
unused 
tracks 
softterm 

The hard term in both ETmiss and L ET is characterised by little dependence on pile-up, as it includes only fully calibrated objects, where the calibration includes a pile-up correction and objects tagged as originating from pile-up are removed. The particular choice of using only tracks from the hardscatter vertex for the soft term strongly suppresses pile-up contributions to this term as well. The observed residual pile-up dependencies are discussed with the performance results shown in Sect. 6. 
3.3 Object selection 
The following selections are applied to reconstructed particles and jets used for the performance evaluations presented 
jet track
p− p(6)
TT 

selected accepted accepted selected accepted unused electrons photons τ -leptons muons jets tracks 
Emiss e Emiss ͣ Emiss soft 

Emiss γ miss had Emiss jet 
T TT
E

TT T 
softtermhardterm 
The Emiss 
T and miss observables can be constructed accordingtoEqs.(3) and (4), respectively, for the overall missing transverse momentum (from Emiss) as well as for each 
T 
individual term indicated in Eq. (6). In the priority-ordered reconstruction sequence for Emiss, contributions are deﬁned 
T 
by a combination of analysis-dependent selections and a possible rejection due to the applied signal ambiguity resolution. 
2 In this formula the notion of selected, which is only applicable to electrons and muons, means that the choice of reconstructed particles is purely given by a set of criteria similar to those given in Sects. 3.3.1 and 3.3.4, respectively, with possible modiﬁcations imposed by a given analysis. The notion of accepted indicates a modiﬁcation of the set of initially selected objects imposed by the signal ambiguity resolution. 
in Sects. 6–8. Generally, these selections require reﬁnements to achieve optimal Emiss reconstruction performance in the 
T 
context of a given physics analysis, and the selections performed in this study are an example set of criteria. 
3.3.1 Electron selection 
Reconstructed electrons are selected on the basis of their shower shapes in the calorimeter and how well their calorimeter cell clusters are matched to ID tracks [7]. Both are evaluated in a combined likelihood-based approach [8]. Electrons with at least medium reconstruction quality are selected. They are calibrated using the default calibration given in 
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Table1 Overview of the contributions to Emiss and L ET from hard 
T 
objects such as electrons (e), photons (γ ), hadronically decaying τ -leptons (had), muons (), and jets, together with the signals for the soft term. The conﬁguration shown is the one used as reference for the performance evaluations presented in this paper. The table is ordered descending in priority Pof consideration for Emiss reconstruction, with 
T 
(1) being the ﬁrst and (5) being the last calculated hard-object contri
bution. The soft-event contribution is constructed at the lowest priority (6), after all hard objects are considered. The transverse (longitudinal) impact parameter d0(z0 sinω ) used to select the ID tracks contributing 
to Emiss soft and L Esoft 
T Tin P = 6 is measured relative to the hardscatter vertex. All variables are explained in Sect. 3.2. The angular distance Rbetween objects is deﬁned as R = χ 2 + ψ 2 
P Objects contributing to Emiss and L ET
T 

Type  Selections Variables Comments  
(1)  e  || Ϫ 1 37 or 1 52 Ϫ || Ϫ 2 47 pT Ϭ 10 GeV Emiss e T L Ee T All e 
(2)  γ  || Ϫ 1 37 or 1 52 Ϫ || Ϫ 2 47 pT Ϭ 25 GeV Emiss γ T L Eγ T  
(3)  had  || Ϫ 1 37 or 1 52 Ϫ || Ϫ 2 47 pT Ϭ 20 GeV Emiss had T L Ehad T  
(4)  ͣ  || Ϫ 2 7 pT Ϭ 10 GeV Emiss ͣ T L Eͣ T  
(5)  Jet  || <BR>Ϫ <BR>4 5 pT Ϭ <BR>60 GeV Emiss jet T L <BR>Ejet T Sect. 3.3.6  
or  
2 4 Ϫ || Ϫ 4 5 20 GeV Ϫ pT Ϫ 60 GeV  
or  
|| Ϫ 2 4 20 GeV Ϫ pT Ϫ 60 GeV JVT Ϭ 0 59  
(6)  ID track  pT Ϭ 400 MeV Emiss soft T  
|d0| Ϫ 1 5mm |z0 sinω | Ϫ 1 5mm Rtrack e − γ cluster Ϭ 0 05 Rtrack had Ϭ 0 2 L Esoft T  

± passing medium reconstruction quality and kinematic selections 
All γ passing tight quality and kinematic selections in reconstruction, and without signal overlap with (1) 
All had passing medium reconstruction quality and kinematic selections, and without signal overlap with (1) and (2) 
All ͣ passing medium quality and kinematic selections in reconstruction; for the discussion of the –jet overlap removal see 
All jets passing reconstruction quality (jet cleaning) and kinematic selections, and without signal overlapa with (1)–(3); for the dedicated overlap removal strategy with ͣ from (4) see Sect. 3.3.6 
All ID tracks from the hard-scatter vertex passing reconstruction quality and kinematic selections, and not associated with any particle from (1), (3) or (4), or ghost-associated with a jet from (5) 

aWhile for single reconstructed particles no overlap is accepted at all, jets with a signal overlap fraction E Ϫ 50% can still contribute their associated tracks to Emiss soft if those pass the selections for P = 6 , as discussed in Sect. 3.3.5. The deﬁnition of E is giveninEq. (8)
T 

Ref. [7]. To be considered for Emiss reconstruction, elec-deposit by electrons within 1 37 Ϫ || Ϫ 1 52 is likely recon-
T 

trons passing the reconstruction quality requirements are in structed as a jet and enters the Emiss reconstruction as such, if 
T 

addition required to have pT Ϭ 10 GeV and || Ϫ 1 37 or this jet meets the corresponding selection criteria discussed 1 52 Ϫ || Ϫ 2 47, to avoid the transition region between the in Sect. 3.3.5. central and endcap electromagnetic calorimeters. Any energy 
123 


3.3.2 Photon selection 
The identiﬁcation and reconstruction of photons exploits the distinctive evolution of their electromagnetic showers in the calorimeters [9]. Photons are selected and calibrated using the tight selection criteria given in Ref. [7]. In addition to the reconstruction quality requirements, photons must have pT Ϭ 25 GeV and || Ϫ 137 or 152 Ϫ || Ϫ 2 37 to be included in the Emiss reconstruction. Similarly to electrons, 
T photons emitted within 1 37 Ϫ || Ϫ 1 52 may contribute to Emiss 
T as a jet. 
3.3.3 τ -Lepton selection 
Hadronically decaying τ -leptons are reconstructed from narrow jets with low associated track multiplicities [10]. Candidates must pass the medium quality selection given in Ref. [11], and in addition have pT Ϭ 20 GeV and || Ϫ 1 37 or 1 52 Ϫ || Ϫ 2 47. Any τ -lepton not satisfying these τ identiﬁcation criteria may contribute to Emiss when passing 
T the jet selection. 
3.3.4 Muon selection 
Muons are reconstructed within || Ϫ 2 5 employing a combined MS and ID track ﬁt. Outside of the ID coverage, muons are reconstructed within 2 5 Ϫ || Ϫ 2 7 from a track ﬁt to MS track segments alone. Muons are further selected for 
Emiss 
T reconstruction by requiring the medium reconstruction quality deﬁned in Ref. [12], pT Ϭ 10 GeV, and an association with the hard-scatter vertex for those within || Ϫ 2 5. 
3.3.5 Jet selection 
Jets are reconstructed from clusters of topologically connected calorimeter cells (topo-clusters), described in Ref. [6]. The topo-clusters are calibrated at the electromagnetic (EM) energy scale.3 The anti-kt algorithm [13], as provided by the FastJet toolkit [14], is employed with a radius param-eter R = 0 4 to form jets from these topo-clusters. The jets are fully calibrated using the EM+JES scheme [15] including a correction for pile-up [16]. They are required to have pT Ϭ 20 GeV after the full calibration. The jet contribution to 
Emiss 
T and L ET is primarily deﬁned by the signal ambiguity resolution. 
Jets not rejected at that stage are further ﬁltered using a tagging algorithm to select hard-scatter jets (“jet vertex tagging”) [16]. This algorithm provides the jet vertex tagger 
3 On this scale the energy deposited in the calorimeter by electrons and photons is represented well. The hadron signal at the EM scale is not corrected for the non-compensating signal features of the ATLAS calorimeters. 
variable JVT, ranging from 0 (pile-up-like) to 1 (hard-scatterlike), for each jet with matched tracks.4 The matching of tracks with jets is done by ghost association, where tracks are clustered as ghost particles into the jet, as described in Ref. [3] and based on the approach outlined in Ref. [17]. 
The overlap resolution can result in a partial overlap of the jet with an electron or photon, in terms of the fraction of common signals contributing to the respective reconstructed energy. This is measured by the ratio E of the electron(photon) energy EEM to the jet energy EEM,
eγ jet 
EEM E = eγ (8)
EEM 
jet 
with both energies calibrated at the EM scale. In the case of E ↕ 50%, the jet is included in Emiss reconstruction,
T 
with its pT scaled by 1 − E.For E Ϭ 50%, only the tracks associated with the jet, excluding the track(s) associated with the overlapping particle if any, contribute to the soft term as discussed in Sect. 3.4. 
Jets not rejected by the signal ambiguity resolution and with pT Ϭ 20 GeV and || Ϭ 2 4, or with pT ↖ 60 GeV and || Ϫ 4 5, are always accepted for Emiss reconstruction.
T 
Jets reconstructed with 20 GeV Ϫ pT Ϫ 60 GeV and || Ϫ 2 4 are only accepted if they are tagged by JVT Ϭ 0 59. In both cases, the jet pT thresholds are applied to the jet pT before applying the E correction. Additional conﬁgurations for selecting jets used in Emiss reconstruction are discussed in 
T 
Appendix A, together with the effect of the variation of these selection criteria on the Emiss reconstruction performance. 
T 
3.3.6 Muon overlap with jets 
Jets overlapping with a reconstructed muon affect the Emiss 
T 
reconstruction in a manner that depends on their origin. If these jets represent a signiﬁcant (catastrophic) energy loss along the path of the muon through the calorimeter, or if they are pile-up jets tagged by JVT as originating from the hardscatter interaction due to the muon ID track, they need to be rejected for Emiss reconstruction. On the other hand, jets 
T 
reconstructed from ﬁnal state radiation (FSR) off the muon need to be included into ETmiss reconstruction. 
In all cases, the muon–jet overlap is determined by ghostassociating the muon with the jet. For this, each muon enters the jet clustering as ghost particle with inﬁnitesimal small momentum, together with the EM-scale topo-clusters from the calorimeter. If a given ghost particle becomes part of a jet, the corresponding muon is considered overlapping with this 
4 In the calculation of JVT the total amount of pT carried by tracks from the hard-scatter vertex matched to the given jet is related to the total amount of pT carried by all matched tracks, among other inputs, to tag jets from the hard-scatter interaction. 
123 


jet. This procedure is very similar to the track associations with jets mentioned in Sect. 3.3.5. 
Tagging jets using JVT efﬁciently retains those from the hard-scatter vertex for Emiss reconstruction and rejects jets 
T generated by pile-up. A muon overlapping with a pile-up jet can lead to a mis-tag, because the ID track from the muon represents a signiﬁcant amount of pT from the hard-scatter vertex and thus increases JVT. As a consequence of this fake tag, the pile-up jet pT contributes to Emiss, and thus degrades 
T both the Emiss response and resolution due to the stochastic 
T nature of its contribution. A jet that is reconstructed from a catastrophic energy loss of a muon tends to be tagged as a hard-scatter jet as well. This jet is reconstructed from topo-clusters in close proximity to the extrapolated trajectory of the ID track associated with the muon bend in the axial magnetic ﬁeld. Inclusion of such a jet into Emiss reconstruction leads to double-counting of the 
T transverse momentum associated with the muon energy loss, as the fully reconstructed muon pT is already corrected for this effect. To reject contributions from pile-up jets and jets reconstructed from muon energy loss, the following selection criteria are applied: 
ͣ 	jet
• 	p T track Ϭ 0 8 – the transverse momentum of the 
T trackϝ p

ID track associated with the muon (pT track) represents a jet
signiﬁcant fraction of the transverse momentum p
T track, the sum of the transverse momenta of all ID tracks asso
ciated with the jet; 
jet ͣ 	jet
• 	pT ϝ pT track Ϫ 2 – the overall transverse momentum p
T 

of the jet is not too large compared to p
T track; 
• 	NPV 
track Ϫ 5 – the total number of tracks NPV 
track associated with the jet and emerging from the hard-scatter vertex is small. 
All jets with overlapping muons meeting these criteria are understood to be either from pile-up or a catastrophic muon energy loss and are rejected for Emiss reconstruction. The 
T muons are retained for the ETmiss reconstruction. Another consideration for muon contributions to ETmiss is FSR. Muons can radiate hard photons at small angles, which are typically not reconstructed as such because of the nearby muon ID track violating photon isolation requirements. They are also not reconstructed as electrons, due to the mismatch between the ID track momentum and the energy measured by the calorimeter. Most likely the calorimeter signal generated by the FSR photon is reconstructed as a jet, with the muon ID track associated. As the transverse momentum carried by the FSR photon is not recovered in muon reconstruction, jets representing this photon need to be included in the Emiss 
T reconstruction. Such jets are characterised by the following 
selections, which are highly indicative of a photon in the ATLAS calorimeter: 
• 	NPV 
Ϫ 	3 – the jet has low charged-particle content, 
track indicated by a very small number of tracks from the hard
scatter vertex; 
• 	fEMC Ϭ 0 9 – the jet energy Ejet is largely deposited in the electromagnetic calorimeter (EMC), as expected for photons and measured by the corresponding energy 
Ejet
fraction fEMC = EMCEjet; 
jet

• 	pT PS Ϭ 2 5 GeV – the transverse momentum contribujet jet
tion pT PS from presampler signals to pindicates an 
T 
early starting point for the shower; 

• 	jet Ϫ 0 1 – the jet is narrow, with a width jet comparable to a dense electromagnetic shower; jet is reconstructed according to 
i RipT i 
jet = 

ipT i 
where Ri = i 2 + i 2 is the angular distance of topo-cluster i from the jet axis, and pT i is the trans-verse momentum of this cluster; 
jet 	
• 	pϬ 0 8 – the transverse momentum
T trackϝ pT track 
jet

pT track carried by all tracks associated with the jet is 

close to p
T track. 
Jets are accepted for Emiss reconstruction when consistent 
T with an FSR photon deﬁned by the ensemble of these selection criteria, with their energy scale set to the EM scale, to improve the calibration. 
3.4 Emiss 
soft term 
T 
The soft term introduced in Sect. 3.2 is exclusively reconstructed from ID tracks from the hard-scatter vertex, thus only using the pT-ﬂow from soft charged particles. It is an important contribution to Emiss for the improvement of both 
T 
the Emiss 
T scale and resolution, in particular in ﬁnal states with a low hard-object multiplicity. In this case it is indicative of (hadronic) recoil, comprising the event components not otherwise represented by reconstructed and calibrated particles or jets. 
The more inclusive reconstruction of the ETmiss soft term including signals from soft neutral particles uses calorimeter topo-clusters. The reconstruction performance using the calorimeter-based Emiss soft calo is inferior to the track-only-
T 
based Emiss soft 
T , 	mostly due to a larger residual depen
dence on pile-up. More details of the topo-cluster-based Emiss soft calo 
reconstruction are discussed in Sect. 8.1.
T 
123 


3.4.1 Trackand vertex selection 
Hits in the ID are used to reconstruct tracks pointing to a particular collision vertex [18]. Both the tracks and vertices need to pass basic quality requirements to be accepted. Each event typically has a number NPV Ϭ 1 of reconstructed primary vertices. 
Tracks are required to have pT Ϭ 400 MeV and || Ϫ 2 5, in addition to the reconstruction quality requirements given in Ref. [19]. Vertices are constructed from at least two tracks passing selections on the transverse (longitudinal) impact parameter |d0| Ϫ 1 5mm (|z0 sinω | Ϫ 1 5 mm) relative to the vertex candidate. These tracks must also pass requirements on the number of hits in the ID. The hard-scatter vertex is identiﬁed as described in Appendix A. 
3.4.2 Tracksoft term 
The track sample contributing to Emiss soft for each recon-
T structed event is collected from high-quality tracks emerging from the hard-scatter vertex but not associated with any electron, τ -lepton, muon, or jet contributing to Emiss recon-
T struction. The applied signal-overlap resolution removes 
• 	
ID tracks with Rtrack,electron/photon cluster 0 05; 

• 	
ID tracks with Rtrack τ -lepton Ϫ 0 2; 

• 	
ID tracks associated with muons; 

• 	
ID tracks ghost-associated with fully or partially contributing jets. 


ID tracks from the hard-scatter vertex that are associated with jets rejected by the overlap removal or are associated with jets that are likely from pile-up, as tagged by the JVT procedure discussed in Sect. 3.3.5, contribute to Emiss soft .
T Since only reconstructed tracks associated with the hard
scatter vertex are used, the track-based Emiss soft is largely 
T insensitive to pile-up effects. It does not include contributions from any soft neutral particles, including those produced by the hard-scatter interaction. 
4 Data and simulation samples 
The determination of the Emiss reconstruction performance 
T uses selected ﬁnal states without (Emiss true = 0) and 
T with genuine missing transverse momentum from neutrinos (Emiss true 
= pT). Samples with Emiss true = 0 are composed 
TT of leptonic Zboson decays (Z → ee and Z → ) collected by a trigger and event selection that do not depend on the particular pile-up conditions, since both the electron and muon triggers as well as the corresponding reconstructed kinematic variables are only negligibly affected by pile-up. Also using 
lepton triggers, samples with neutrinos were collected from W → eυ and W → υ decays. In addition, samples with neutrinos and higher hard-object multiplicity were collected from top-quark pair (tt¯) production with at least either the t or the t¯ decaying semi-leptonically. 
4.1 Data samples 
The data sample used corresponds to a total integrated luminosity of 3 2fb−1, collected with a proton bunchcrossing interval of 25 ns. Only high-quality data with a well-functioning calorimeter, inner detector and muon spectrometer are analysed. The data-quality criteria are applied, which reduce the impact of instrumental noise and out-oftime calorimeter deposits from cosmic-ray and beam backgrounds. 
4.2 Monte Carlo samples 
The Z → λ and W → υ samples were generated using Powheg-Box [20] (version v1r2856) employing a matrix element calculation at next-to-leading order (NLO) in perturbative QCD. To generate the particle ﬁnal state, the (partonlevel) matrix element output was interfaced to Pythia8 [21],5 which generated the parton shower (PS) and the underlying event (UE) using the AZNLO tuned parameter set [22]. Parton distribution functions (PDFs) were taken from the CTEQ6L1 PDF set [23]. 
The tt¯-production sample was generated with a Powheg NLO kernel (version v2r3026) interfaced to Pythia6 [24] (version 6.428) with the Perugia2012 set of tuned parameters [25] for the PS and UE generation. The CT10 NLO PDF set [26] was employed. The resummation of soft-gluon terms in the next-to-next-to-leading-logarithmic (NNLL) approximation with top++ 2.0 [27] was included. 
Additional processes contributing to the Z → λ and W → υ ﬁnal state samples are the production of dibosons, single top quarks, and multijets. Dibosons were generated using Sherpa [28–31] version v2.1.1 employing the CT10 PDF set. Single top quarks were generated using Powheg version v1r2556 with the CT10 PDF set for the t-channel production and Powheg version v1r2819 for the s-channel and the associated top quark (Wt) production, all interfaced to the PS and UE from the same Pythia6 conﬁguration used for tt¯production. Multijet events were generated using Pythia8 with the NNPDF23LO PDF set [32] and the A14 set of tuned PS and UE parameters described in Ref. [33]. 
Minimum bias (MB) events were generated using Pythia8 with the MSTW2008LO PDF set [34] and the A2 tuned 
5 Version 8.186 was used for all ﬁnal states generated with Pythia8. 
123 


parameter set [35] for PS and UE. These MB events were used to model pile-up, as discussed in Sect. 4.3. 
For the determination of the systematic uncertainties 
Emiss
in T reconstruction, an alternative inclusive sample of Z → ͣ events was generated using the Mad-Graph_aMC@NLO (version v2.2.2) matrix element generator [36] employing the CTEQ6L1 PDF set. Both PS and UE were generated using Pythia8 with the NNPDF23LO PDF set and the A14 set of tuned parameters. 
The MC-generated events were processed with the Geant4 software toolkit [37], which simulates the propagation of the generated stable particles6 through the ATLAS detector and their interactions with the detector material [38]. 
4.3 Pile-up 
The calorimeter signals are affected by pile-up and the short bunch-crossing period at the LHC. In 2015, an average of about 13 pile-up collisions per bunch crossing was observed. The dominant contribution of the additional pp collisions to the detector signals of the recorded event arises from a diffuse emission of soft particles superimposed to the hardscatter interaction ﬁnal state (in-time pile-up). In addition, the LAr calorimeter signals are sensitive to signal remnants from up to 24 previous bunch crossings and one following bunch crossing (out-of-time pile-up), as discussed in Refs. [6,39]. Both types of pile-up affect signals contributing to Emiss.
T The in-time pile-up activity is measured by the number of reconstructed primary collision vertices NPV. The out-oftime pile-up is proportional to the number of collisions per bunch crossing , measured as an average over time periods of up to two minutes by integrated signals from the luminosity detectors in ATLAS [40]. To model in-time pile-up in MC simulations, a number of generated pile-up collisions was drawn from a Poisson distribution around the value of ͣ recorded in data. The collisions were randomly collected from the MB sample discussed in Sect. 4.2. The particles emerging from them were overlaid onto the particle-level ﬁnal state of the generated hard-scatter interaction and converted into detector signals before event reconstruction. The event reconstruction then proceeds as for data. Similar to the LHC proton-beam structure, events in MC simulations are organised in bunch trains, where the structure in terms of bunch-crossing interval and gaps between trains is taken into account to model the effects of out-of-time pile-up. The fully reconstructed events in MC simulation samples are ﬁnally weighted such that the distribution of the number of overlaid collisions over the whole sample corresponds to the ͣ distribution observed in data. 
6 In ATLAS stable particles are those with an expected laboratory life-time τ corresponding to cϬ 10 mm. 
The effect of pile-up on the signal in the Tile calorimeter is reduced due to its location behind the electromagnetic calorimeter and its fast time response [41]. Reconstructed ID and MS tracks are largely unaffected by pile-up. 
5 Event selection 
5.1 Z → ͣ event selection 
The Z → ͣ ﬁnal state is ideal for the evaluation of ETmiss reconstruction performance, since it can be selected with a high signal-to-background ratio and the Z kinematics can be measured with high precision, even in the presence of pile-up. Neutrinos are produced only through very rare heavy-ﬂavour decays in the hadronic recoil. This channel can therefore be considered to have no genuine missing transverse momentum. Thus, the scale and resolution for the reconstructed 
Emiss 
T are indicative of the reconstruction quality and reﬂect limitations introduced by both the detector and the ambiguity resolution procedure. The well-deﬁned expectation value 
Emiss true 
T = 0 allows the reconstruction quality to be deter
mined in both data and MC simulations. The reconstructed Emiss 
in this ﬁnal state is also sensitive to the effectiveness 
T of the muon–jet overlap resolution, which can be explored in this low-multiplicity environment in both data and MC simulations, with a well-deﬁned Emiss.
T Events must pass one of three high-level muon triggers 

with different pT thresholds and isolation requirements. The isolation is determined by the ratio of the scalar sum of pT of reconstructed tracks other than the muon track itself, in a cone of size R = 0 2 around the muon track ( pTcone), to 
ͣ 
pT . The individual triggers require (1) p Ϭ 20 GeV and 
T 
ͣ 
cone cone
pϝ p Ϫ 0 12, or (2) p Ϭ 24 GeV and pϝ p 
TTT TT 

0 06, or (3) pT Ϭ 50 GeV without isolation requirement. 
Theofﬂineselectionof Z → ͣ events requires exactly two muons, each selected as deﬁned in Sect. 3.3.4, with the additional criteria that (1) the muons must have opposite 

charge, (2) pT Ϭ 25 GeV, and (3) the reconstructed invariant mass mͣ of the dimuon system is consistent with the mass mZ of the Z boson, |mͣ − mZ| Ϫ 25 GeV. 
5.2 W → eυ event selection 
Events with W → eυ or W → υ in the ﬁnal state pro-vide a well-deﬁned topology with neutrinos produced in the hard-scatter interaction. In combination with Z → , the effectiveness of signal ambiguity resolution and lepton energy reconstruction for both the electrons and muons can be observed. The W → eυ events in particular provide a good 

metric with Emiss true = pϬ 0 to evaluate and validate 
TT the scale, resolution and direction (azimuth) of the recon
structed Emiss,asthe Emiss reconstruction is sensitive to the 
TT 
123 


electron–jet overlap resolution performance. This metric is 

only available in MC simulations where pT is known. Candidate W → eυ events are required to pass the high-level electron trigger with pT Ϭ 17 GeV. Electron candidates are selected according to criteria described in Sect. 3.3.1.Only events containing exactly one electron are considered. 
Further selections using Emiss and the reconstructed trans-
T verse mass mT, given by 
J 
T eEmiss
mT = 2 pT 1 − cos ψ 
are applied to reduce the multijet background with one jet 
emulating an isolated electron from the W boson. Here Emiss 
T is calculated as presented in Sect. 3. The transverse 
e
momentum of the electron is denoted by pT, and ψ is the distance between miss and the azimuth of the electron. Selected events are required to have Emiss Ϭ 25 GeV and 
T mT Ϭ 50 GeV. 
5.3 tt¯event selection 
Events with tt¯in the ﬁnal state allow the evaluation of the Emiss 
T performance in interactions with a large jet multiplicity. Electrons and muons used to deﬁne these samples are reconstructed as discussed in Sects. 3.3.1 and 3.3.4, respec<BR>tively, and are required to have pT Ϭ 25 GeV. 
The ﬁnal tt¯sample is selected by imposing additional requirements. Each event must have exactly one electron and no muons passing the selections described above. In addition, at least four jets reconstructed by the anti-kt algorithm with R = 0 4 and selected following the description in Sect. 3.3.5 are required. At least one of the jets needs to be b-tagged using the tagger conﬁguration for a 77% efﬁciency working point described in Ref. [42]. All jets are required to be at an angular distance of R Ϭ 0 4 from the electron. 
6 Performance of ETmissreconstructionin data and MonteCarlo simulation 
Unlike for fully reconstructed and calibrated particles and jets, and in the case of the precise reconstruction of charged particle kinematics provided by ID tracks, Emiss reconstruc-
T tion yields a non-linear response, especially in regions of phase space where the observation bias discussed in Sect. 3.1 dominates the reconstructed ETmiss. In addition, the ETmiss resolution functions are characterised by a high level of 
complexity, due to the composite character of the observable. Objects with different pT-resolutions contribute, and the Emiss 
T composition can ﬂuctuate signiﬁcantly for events from the same ﬁnal state. Due to the dependence of the Emiss 
T response on the resolution, both performance characteris
tics change as a function of the total event activity and are affected by pile-up. There is no universal way of mitigating these effects, due to the inability to validate in data a stable and universal calibration reference for ETmiss. 
Emiss
The T reconstruction performance is therefore assessed by comparing a set of reconstructed Emiss-related
T observables in data and MC simulations for the same ﬁnalstate selection, with the same object and event selections applied. Systematic uncertainties in the Emiss response and 
T resolution are derived from these comparisons and are used to quantify the level of understanding of the data from the physics models. The quality of the detector simulation is 
independently determined for all reconstructed jets, particles and ID tracks, and can thus be propagated to the overall Emiss 
T uncertainty for any given event. Both the distributions of observables as well as their average behaviour with respect to relevant scales measuring the overall kinematic activity of the hard-scatter event or the pile-up activity are compared. To focus on distribution shapes rather than statistical differences in these comparisons, the overall distribution of a given observable obtained from MC simulations is normalised to the integral of the corresponding distribution in data. 
As the reconstructed ﬁnal state can be produced by different physics processes, the individual process contributions in MC simulations are scaled according to the cross section of the process. This approach is taken to both show the contribution of a given process to the overall distribution, and to identify possible inadequate modelling arising from any individual process, or a subset of processes, by its effect on the overall shape of the MC distribution. 
Inclusive event samples considered for the Emiss perfor-
T mance evaluation are obtained by applying selections accord
ing to Sect. 5.1 for a ﬁnal state without genuine Emiss 
T (Z → ), and according to Sect. 5.2 for a ﬁnal state with 
genuine Emiss (W → e). From these, speciﬁc exclusive 
T samples are extracted by applying conditions on the number of jets reconstructed. In particular, zero jet (Njet = 0) samples without any jet with pT Ϭ 20 GeV (fully calibrated) and || Ϫ 4 9 are useful for exclusively studying the performance of the soft term. Samples with events selected on the basis of a non-zero number of reconstructed jets with pT Ϭ 20 GeV are useful for evaluating the contribution of jets to Emiss.
T While the pT response of jets is fully calibrated and provides a better measurement of the overall event pT-ﬂow, the pT resolution for jets is affected by pile-up and can introduce a detrimental effect on Emiss reconstruction performance. 
T Missing transverse momentum and its related observables presented in Sect. 3.1 are reconstructed for the performance evaluations shown in the following sections using a standard reconstruction conﬁguration. This conﬁguration implements the signal ambiguity resolution in the Emiss recon-
T struction sequence discussed in Sect. 3.1. It employs the hard-object selections deﬁned in Sects. 3.3.1–3.3.4, with jets selected according to the prescriptions given in Sect. 3.3.5. 
123 


The overlap resolution strategy for jets and muons described in Sect. 3.3.6 is applied. The soft term is formed from ID tracks according to Sect. 3.4. 
6.1 Emiss 
T modelling in Monte Carlo simulations 
, Emiss, Emiss
The quality of the MC modelling of Emiss and
xy T L ET, reconstructed as given in Eqs. (1), (3) and (5), is eval<BR>uated for an inclusive sample of Z → ͣ events by comparing the distributions of these observables to data. The results are presented in Fig. 1. The data and MC simula<BR>tions agree within 20% for the bulk of the Emiss distribu-
T tion shown in Fig. 1a, with larger differences not accommo<BR>dated by the total (systematic and statistical) uncertainties of the distributions for high Emiss. These differences sug-
T gest a mismodelling in tt¯ events, the dominant background in the tail regime [43]. The L ET distributions compared between data and MC simulations in Fig. 1b show discrep<BR>ancies signiﬁcantly larger than the overall uncertainties for 200 GeV L ET Ϫ 1 2 TeV. These reﬂect the level of mismodelling of the ﬁnal state mostly in terms of hard-object and Emiss
composition in MC simulations. The Emiss spec
xy 
tra shown in Fig. 1c, d, respectively, show good agreement between data and MC simulations for the bulk of the distributions within |Emiss| Ϫ 100 GeV, with larger differences 
xy 
observed outside of this range still mostly within the uncertainties. The distributions of individual contributions to ETmiss from jets (Emiss jet 
), muons (Emiss ), and the soft term (Emiss soft ),
TT T as deﬁned in Eq. (6), are compared between data and MC sim<BR>ulations for the same inclusive Z → ͣ sample in Fig. 2. Agreement between data and MC simulations for Emiss jet in
T Fig. 2a is of the order of ±20% and within the total uncer
tainties for Emiss jet � 120 GeV, but beyond those for higher 
T 
Emiss jet 
. A similar observation holds for Emiss ͣ in Fig. 2b,
TT where data and MC simulations agree within the uncertain
ties for low Emiss ͣ but signiﬁcantly beyond them for larger 
T 
Emiss ͣ 
T . Agreement between data and MC simulations is bet-ter for the soft term Emiss soft , with differences up to 10% for 
T 
Emiss soft 
T � 30 GeV, as seen in Fig. 2c. Larger differences for larger Emiss soft 
are still found to be within the uncertainties. 
T The peak around Emiss jet = 20 GeV indicates the onset 
T of single-jet events at the threshold pT = 20 GeV for jets 
contributing to Emiss jet . Larger values of Emiss jet arise from 
TT events with one or more high-pT jets balancing the pT of the 
Z boson. 
For the W → eυ sample with genuine missing transverse 
momentum given by pT, both the total reconstructed Emiss 
T and the soft term are compared between data and MC simu
lations in Fig. 3. The level of agreement between the Emiss 
T distributions for data and MC simulations shown in Fig. 3a 
for the inclusive event sample is at ±20%, similar to that observed for the Z → ͣ sample in Fig. 1a, except that for this ﬁnal state it is found to be within the total uncertainties of the measurement. The differences between the ETmiss distributions observed with the exclusive Njet = 0 sample shown in Fig. 3b are well below 20%, but show a trend to larger discrepancies for decreasing Emiss � 40 GeV. This 
T trend is due to the missing background contribution in MC simulations from multijet ﬁnal states. The extraction of this contribution is very inefﬁcient and only possible with large statistical uncertainties. Even very large MC samples of multijet ﬁnal states provide very few events with only one jet that is accidentally reconstructed as an electron, and with the amount of Emiss required in the W → eυ selection described 
T in Sect. 5.2. The comparison of the Emiss soft distributions 
T from data and MC simulations shown in Fig. 3c yields agree<BR>
ment well within the uncertainties, for Emiss soft 2 10 GeV. 
T The rising deﬁciencies observed in the MC distribution for 
decreasing Emiss soft � 10 GeV are expected to be related to 
T the missing multijet contribution. 
6.2 Emiss 
T response and resolution 
The response in the context of Emiss reconstruction is deter-
T mined by the deviation of the observed Emiss from the expec-
T tation value for a given ﬁnal state. This deviation sets the scale 
for the observed Emiss. If this deviation is independent of the 
T genuine missing transverse momentum, or any other hard 
pT indicative of the overall hard-scatter activity, the Emiss 
T response is linear. In this case, a constant bias in the recon
structed Emiss is still possible due to detector inefﬁciencies 
T and coverage (acceptance) limitations. Final states balanced in transverse momentum are expected to show a non-linear Emiss response at low event activity, as 
T the response in this case suffers from the observation bias in Emiss 
reconstruction discussed in Sect. 3.1. With increas-
T ing momentum transfers in the hard-scatter interaction, the Emiss 
T response becomes increasingly dominated by a wellmeasured hadronic recoil and thus more linear. In the case of ﬁnal states with genuine missing transverse momentum, 
the Emiss 
response is only linear once Emiss true exceeds the 
TT observation bias. These features are discussed in Sect. 6.2.1 and explored in Sect. 6.2.2. Contributions to the ﬂuctuations in the ETmiss measurement arise from (1) the limitations in the detector acceptance not allowing the reconstruction of the complete transverse momentum ﬂow from the hard interaction, (2) the irreducible intrinsic signal ﬂuctuations in the detector response, and from 
(3) the additional response ﬂuctuations due to pile-up. In particular (1) introduces ﬂuctuations driven by the large variations of the particle composition of the ﬁnal state with respect to their types, momenta and directions. The limited detector 
123 



L ET, c Emiss and d Emiss
Fig. 1 Distributions of a ETmiss, b xy for an inclusive sample of Z → ͣ events extracted from data and compared to MC simulations including all relevant backgrounds. The shaded areas indicate the total uncertainty for MC simulations, including the overall statistical uncertainty combined with systematic uncertainties from the 
coverage of || Ϫ 4 9 for all particles, together with the need to suppress the pile-up-induced signal ﬂuctuations as much as possible, restricts the contribution of particles to Emiss to
T 
the reconstructed and accepted e, γ , had and , and those 
pT scale and resolution which are contributed by muons, jets, and the soft term. The last bin of each distribution includes the overﬂow, and the ﬁrst bin contains the underﬂow in cand d. The respective ratios between data and MC simulations are shown below the distributions, with the shaded areas showing the total uncertainties for MC simulations 
being part of a reconstructed and accepted jet. In addition, the pT-ﬂow of not explicitly reconstructed charged particles emerging from the hard-scatter vertex is represented by ID tracks contributing to Emiss soft given in Eqs. (6) and (7), but 
T 
123 



Fig. 2 Distributions of athe jet term Emiss jet , bthe muon term Emiss ͣ ,
TT and c the soft term Emiss soft for the inclusive samples of Z → 
T 
events in data, compared to MC simulations including all relevant backgrounds. The shaded areas indicate the total uncertainty from MC simulations, including the overall statistical uncertainty combined with the 
only in the phase space deﬁned by the selections given in Sect. 3.4.1. All other charged and neutral particles do not contribute to ETmiss reconstruction. 
respective systematic uncertainties from athe jet, bthe muon, and cthe soft term. The last bin of each distribution includes the overﬂow entries. The respective ratios between data and MC simulations are shown below the distributions, with the shaded areas showing the corresponding total uncertainties from MC simulations 
Like for the Emiss response, resolution-related aspects 
T 
of Emiss 
reconstruction are understood from data-to-MC
T 
simulations comparisons. The scales used for the correspond
123 



Fig. 3 Distributions of the total Emiss in a theinclusive caseand b
T the Njet = 0 case, as well as c the soft term Emiss soft reconstructed
T 
in Njet = 0eventswith W → eυ in data. The expectation from MC simulation is superimposed and includes all relevant background ﬁnal states passing the event selection. The inclusive Emiss distribution from 
T 
MC simulations contains a small contribution from multijet ﬁnal states at low Emiss, which is absent for the Njet = 0 selection. The shaded 
T 
areas indicate the total uncertainty for MC simulations, including the overall statistical uncertainty combined with systematic uncertainties comprising contributions from the electron, jet, and the soft term. The last bins contain the respective overﬂows. The respective ratios between data and MC simulations are shown below the distributions, with the shaded areas indicating the total uncertainties for MC simulations 
123 


ing evaluations are the overall event activity represented by L ET, and the pile-up activity measured by NPV. The measurement of the ETmiss resolution is discussed in Sect. 6.2.3 and results are presented in Sect. 6.2.4. 
Emiss
6.2.1 scale determination 
T 
In events with Z →ͣ decays, the transverse momentum of the Z boson ( pT Z) is an indicator of the hardness of the interaction. It provides a useful scale for the evaluation of the 
Emiss 
T response for this ﬁnal state without genuine missing 
transverse momentum. The direction of the corresponding Z Z
boson transverse momentum vector pT deﬁnes an axis AZ in the transverse plane of the collision, which is reconstructed from the pT of the decay products by 
+ − 
p +ppZ 
AZ =  TT = T (9) 
 + −  Z
p
 p +p  T
TT
The magnitude of the component of Emiss parallel to AZ is
T 
PZ Emiss
=·AZ (10) 
f T 
This projection is sensitive to any limitation in Emiss recon-
T 
struction, in particular with respect to the contribution from 
Z
the hadronic recoil against pT , both in terms of response and resolution. Because it can be determined both for data and MC simulations, it provides an important tool for the validation of the Emiss response and the associated systematic 
T 
uncertainties. 
The expectation value for a balanced interaction producing a Z boson against a hadronic recoil is E[PfZ]=0. Any observed deviation from this value represents a bias in the Emiss reconstruction. For PfZϪ 0, the reconstructed 
T hadronic activity recoiling against pT Z is too small, while for PfZϬ 0 too much hadronic recoil is reconstructed. The evo
lution of PfZas a function of the hardness of the Z boson production can be measured by evaluating the mean (PfZ)in 
hard Z
bins of the hard-scatter scale pT =pT. In addition to measuring the Emiss response in data and 
T 
MC simulation without genuine Emiss, its linearity can be 
T 
determined using samples of ﬁnal states with genuine Emiss 
T 
in MC simulations. This is done by evaluating the relative deviation lin of the reconstructed Emiss from the expected 
TT 
Emiss true 
Ϭ 0 as a function of Emiss true ,
TT 
Emiss −Emiss true 
lin TT 

T Emiss true 
= (11)
T Emiss true 
T 

6.2.2 MeasuringtheE miss response 
T 
Z
Figure 4 shows (PfZ)as a function of pfor the Njet =0
T and the inclusive Z →ͣ sample, respectively. MC simulations compare well with the data for Njet =0, but show larger deviations up to 30% for the inclusive selection. Nevertheless, these differences are still found to be within the total uncertainty of the measurement. 
Z
The steep decrease of (PfZ)with increasing pin the 
T Njet =0 sample seen in Fig. 4a reﬂects the inherent under<BR>estimation of the soft term, as in this case the hadronic recoil is exclusively represented by ID tracks with pT Ϭ 400 MeV within ||Ϫ 2 5. It thus does not contain any signal from (1) neutral particles, (2) charged particles produced with ||Ϭ 2 5, and (3) charged particles produced within ||Ϫ 2 5but with pT below threshold, rejected by the track quality requirements, or not represented by a track at all due to insufﬁcient signals in the ID (e.g., lack of hits for track ﬁtting). In the case of the inclusive sample shown in Fig. 4b, the Emiss Z
response is recovered better as pT increases, since an 
T increasing number of events enter the sample with a reconstructed recoil containing fully calibrated jets. These pro-vide a more complete representation of the hadronic trans-verse momentum ﬂow. The residual offsets in (PfZ)of about 8 GeV in data and 6 GeV in MC simulations observed for 
Z
pT 2 40 GeV in Fig. 4b agree within the uncertainties of this measurement. 
The persistent bias in (PfZ)is further explored in Fig. 5, which compares variations of (PfZ)respectively using the full Emiss
, the soft-term contribution Emiss soft only, the hard-
TT 
−Emiss soft 
term contribution EmissT T , and the true soft term Emiss true soft Z
only, as a function of pT ,for the Z →
T sample from MC simulations. In particular the difference and Emiss soft 
between the projections using Emiss true soft 
TT indicates the lack of reconstructed hadronic response, when Emiss soft Emiss true soft 
T =T is expected for a fully measured recoil. The parallel projection using only the soft terms is larger than zero for all pT Z due to the missing Z-boson con-
Z
tribution to Emiss given by −p
T T. The deviation from linearity in Emiss reconstruction, mea-
T sured by lin given in Eq. (11), is shown as a function 
T 
of Emiss true 
T for MC simulations of W →e, W →υ and tt¯production in Fig. 6. The observed lin Ϭ 0at 
T 
low Emiss true 
indicates an overestimation of Emiss true by
TT the reconstructed ETmiss due to the observation biases arising from the ﬁnite Emiss resolution, as discussed in Sect. 3.1.
T This bias overcompensates the lack of reconstructed pT-ﬂow from the incompletely measured hadronic recoil in W →eυ and W →υ events for ETmiss true 40 GeV with an increasing non-linearity observed with decreasing Emiss true .
T 
For Emiss true 
2 70 GeV the Emiss response is directly pro-
TT 
123 



Fig. 4 The average projection of Emiss onto the direction AZ of the Z
T 
boson’s transverse momentum vector pT Z,asgiven in Eq.(10), is shown 
ZZ
as a function of p=|pT |in Z →ͣ events from athe Njet =0sam
T 
ple and from bthe inclusive sample. In both cases data are compared to MC simulations. The ratio of the averages from data and MC simula-

ZZ
as a function of p=|pT |in Z →ͣ events from the inclusive MC 
T sample. The average projection of the soft term and the true soft term 
are also shown, to demonstrate the source of the deviation from zero 
portional to Emiss true , with the reconstructed recoil being 
T approximately 2% too small. The W →eυ and W →υ T Emiss true 
ﬁnal states show very similar lin , thus indicating 
T the universality of the recoil reconstruction and the indepen
dence on the lepton ﬂavour of the reconstructed Emiss in a 
T low-multiplicity ﬁnal state with Emiss true Ϭ 0.
T 
tions are shown below the plots. The shaded areas indicate the overall statistical uncertainty combined with systematic uncertainties comprising contributions from the muon and soft-term systematic uncertainties in a, and including the additional jet systematic uncertainties in b,for MC simulations 

Fig. 6 The deviation of the Emiss response from linearity, measured 
T by lin
as a function of the expected Emiss true in Eq. (11), in W →e,
TT W →,and tt¯ﬁnal states in MC simulations. The lower plot shows 
a zoomed-in view on the lin dependence on Emiss true with a highly 
TT suppressed ordinate 
In tt¯ﬁnal-state reconstruction, resolution effects tend at Emiss true 
to dominate lin 120 GeV. Compared to 
TT the W → eυ and W → υ ﬁnal states, a signiﬁ
123 


cantly poorer Emiss resolution is observed in this kinematic 
T region, due to the presence of at least four jets with relatively low pT and high sensitivity to pile-up-induced ﬂuctuations in each event of the tt¯sample. For Emiss true 
T 
T Emiss true 
120 GeV, lin ≈ 2% indicates a proportional 
T 
Emiss 
T response with a systematic shift similar to the one observed in inclusive W-boson production. 
6.2.3 Determination oftheE Tmiss resolution 
The Emiss 
T resolution is determined by the width of the com
bined distribution of the differences between the measured Emiss 
xy and the components of the true missing transverse Emiss true Emiss true 
momentum vector Emiss true = xy .The 
T width is measured in terms of the RMS, with 
⎧ 
Emiss − Emiss true t sample Emiss true 
⎨ RMS W → eυ or t¯Ϭ 0
xyxy T 
RMSmiss 
= 
xy �� 
⎩ Emiss true Z → ͣ sample Emiss true 
RMS =0
xy T 
(12) 
This metric does not capture all of the effects driving the ﬂuctuations in ETmiss reconstruction, such as biases between individual Emiss terms or the behaviour of outliers, but it is 
T an appropriate general measure of how well Emiss represents
T 
Emiss true 
T. 
Using the Z → ͣ sample allows direct comparisons of RMSmiss 
between data and MC simulations, as Emiss true = 0
xy T in this case. The resolution in ﬁnal states with genuine Emiss 
T is determined with MC simulations alone. For W → eυ and 
tt¯ﬁnal states, Emiss true = pis used. 
xyxy 
Emiss
6.2.4 resolution measurements 
T 
The Emiss 
resolution measured by RMSmiss is evaluated as 
T xy a function of the event activity measured by L ET given in 
Eq. (7). For the inclusive Z → ͣ sample, Fig. 7ashows RMSmiss 
xy quickly rising from less than 5 GeV to about 10 GeV with increasing L ET within 50 GeV ↕ L ET Ϫ 70 GeV.7 This is due to the fact that in this range the two muons are the dominant hard objects contributing, with a pT resolution 

proportional to ( p 2. A convolution of the muon resolu-
T tion with a small contribution from Emiss soft is possible for 
T L ET Ϭ 50 GeV. This component is on average about 60% Z
of pT , and subject to the stochastic ﬂuctuations further discussed below. The increase of Z → +1 jet topologies in the Z → ͣ sample leads to an additional source of ﬂuctuations affecting 
7 This lower boundary of this range is given by the muon selection with 
ͣ 
pT Ϭ 25 GeV, as described in Sect. 5.1, assuming no other hard-scatter vertex tracks, i.e. Emiss soft = 0. The upper boundary indicates the lower 
T jet
limit of L ET to accommodate at least one jet with pT Ϭ 20 GeV in addition the two muons (for the jet selection see Sect. 3.3.5). 
RMSmiss 
xy L ET for 70 GeV L ET 180 GeV. In gen
eral the Z → ͣ sample collected for this study covers Z
pT 140 GeV with relevant statistics. At this limit it is expected that the hadronic recoil contains two reconstructed jets, with the onset of this contribution at L ET of about 180 GeV. The corresponding change of the dominant ﬁnal state composition for L ET Ϭ 180 GeV leads to a change of shape of RMSmissL ET , as the transverse momentum of 
xy 
the individual jets rises and the number of contributing jets slowly increases. The expected RMSmissL ET ∝√ L ET
xy scaling driven by the jet-pT resolution [44] therefore dom
inates RMSmiss at these higher L ET. The MC predictions 
xy for RMSmissL ET agree with the data within a few percent 
xy 
and well within the total uncertainties of this measurement. A tendency for slightly poorer resolution in MC simulations is observed, in particular for L ET Ϭ 200 GeV. 
Any contribution from pile-up to RMSmiss is expected 
xy 
to be associated with the jets. While dedicated corrections applied to the jets largely suppress pile-up contributions in the jet response, residual irreducible ﬂuctuations introduced into the calorimeter signals by pile-up lead to a degradation of the jet energy resolution and thus poorer resolution in the jetpT measurement. The dependence of RMSmiss on the pile-up 
xy activity measured by NPV is shown in Fig. 7b. Data show a 
less steep slope of RMSmiss than MC simulations, but 
xy NPV with about 10% worse resolution in the low pile-up region of NPV 5. The resolution in data is better than in MC simulations by about 10% for the region of higher pile-up activity at NPV ≈ 20. The differences between data and MC simulations seen in RMSmissL ET for the inclusive Z → ͣ sample can 
xy 
be further analysed by splitting the sample according to the value of Njet. Figure 8a shows the dependence of RMSmiss 
xy on L ET for Z → ͣ events with Njet = 0. The dominant source of ﬂuctuations other than the muon-pT resolution is in this case introduced by the incomplete reconstruction of the hadronic recoil. These ﬂuctuations increase with increasing pT Z, which in turn means higher overall event activity measured by L ET. For this sample RMSmiss in data compares 
xy 
well to MC simulations, at a level of a few percent, without any observed dependence on L ET. 
The exclusive Njet ↖ 1 samples extracted from Z → ͣ data and MC simulations show the expected RMSmiss ∝
xy
√ 
L ET scaling in Fig. 8b. The resolution in data is well rep<BR>resented by MC simulations, at the level of a few percent. The slightly better resolution observed in data with increasing L ET follows the trend observed in Fig. 7a. The similar trends are expected as this kinematic region is largely affected by the jet contribution. 
The dependence of RMSmiss on NPV shown in Fig. 8c
xy indicates that the Emiss resolution is basically independent 
T of pile-up, for the Njet = 0 sample. This is expected from the 
123 



Fig. 7 The RMS width of the Emiss distributions a in bins of L ET
xy 
and bin bins of the number of primary vertices in an inclusive sample of Z → ͣ events. Predictions from MC simulations are overlaid on 
exclusive Emiss composition comprising the (track-based) 
T 
Emiss ͣ and Emiss soft 
T T terms only. Data and MC simulations compare well within a few percent, and without any observable dependence on NPV. Figure 8dshows the NPV dependence of RMSmiss for the Njet ↖ 1 sample. Comparing this 
xy 
result to Fig. 7b conﬁrms that all pile-up dependence of the Emiss 
T resolution is arising from the jet term. Both trend and magnitude of the data-to-MC comparison follow the observation from the inclusive analysis. 
Emiss
6.2.5 resolutionin ﬁnal states with neutrinos 
T 
The Emiss 
T resolution for ﬁnal states with ETmiss true Ϭ 0is measured by RMSmiss according to Eq. (12) and evaluated 
xy using dedicated inclusive W → eυ and W → υ samples from MC simulations, and the inclusive tt¯MC sample deﬁned in Sect. 5.3. For these samples, RMSmiss can be 
xy 
determined as a function of Emiss true = pT. The dedicated 
T W → eυ and W → υ samples are obtained with an event selection based on the description in Sect. 5.2, but omitting both the Emiss-based and the mT-based selections. 
T Figure 9 shows RMSmiss evaluated as a function of 
xy Emiss true 
T for these samples. The universality of the response to the hadronic recoil observed in Fig. 6, together with the dif<BR>ferent but subdominant contributions from the pT resolutions of the electrons and muons, yield a very similar Emiss resolu-
T tion for W → eυ and W → υ ﬁnal states. Generally, poorer 
the data points, and the ratios are shown below the respective plot. The shaded bands indicate the combined statistical and systematic uncertainties of the resolution measurements 
resolution is observed in tt¯ ﬁnal states. The deviation from 
J 
Emiss true 
the expected RMSmiss ∝ scaling behaviour for 
xy T W → υ at lower Emiss true 
reﬂects the kinematic features of 
T the W boson and its decay. Events with low pT W, and therefore small hadronic recoil, lie predominantly in the region 

25 GeV pT 50 GeV. Since the hadronic recoil is generally the poorly measured component of an event and the reconstructed Emiss is dominated by the lepton pT in this 
T region, the Emiss resolution tends to be better here than for 
T 

events with larger hadronic recoil populating pT 25 GeV 

and pT 2 50 GeV. 
6.3 Emiss 
tails
T 
Large reconstructed Emiss is an indicator for the production 
T of (potentially new) undetectable particles, but can also be generated by detector problems and/or poor reconstruction of the objects used for its reconstruction. Enhanced tails in the distribution of the Emiss components for ﬁnal states with 
T well-known expectation values for Emiss are indicative of 
T such inefﬁciencies. Non-Gaussian shapes in the distribution arise from a combination of object selection inefﬁciencies and potentially non-Gaussian resolutions of the ETmiss constituents. Even for a well-deﬁned ﬁnal state, event-by-event ﬂuctuations in terms of which particles, jets, and soft tracks enter the Emiss recon-
T struction, and with which pT, lead to deviations from a nor,Emiss
mally distributed (Emiss ) response. 
xy 
123 



The Emiss
Fig. 8 resolution RMSmiss determined for a an exclusive 
T xy 
Z → ͣ sample without jets with pT Ϭ 20GeV (Njet = 0) and for b an exclusive sample with at least one jet above this threshold (Njet ↖ 1), as a function of L ET in data and MC simulations. The dependence of 
,Emiss
Figure 10 shows the combined (Emiss ) distribution 
xy 
for the inclusive Z → ͣ sample from MC simulations. To illustrate its symmetric nature and its deviation from a normal distribution in particular with respect to the tails, Gaussian functions are ﬁtted to two limited ranges around the centre of the distribution, ±1 × RMS and ±2 × RMS. 
RMSmiss 
xy on the pile-up activity, as measured by NPV,for thesetwo samples is shown in c and d, respectively. The shaded bands indicate the combined statistical and systematic uncertainties associated with the measurement 
The differences between these functions and the data distribution (lower panel of Fig. 10) indicate a more peaked shape around the most probable value for Emiss with near exponen
xy 
tial slopes. The result of this comparison supports the choice of RMSmiss deﬁned in Eq. (12) in Sect. 6.2.3 for the deter
xy mination of the Emiss resolution, rather than using any of 
T 
123 




and Emiss
xy 
Z → ͣ from simulation. Gaussian ﬁts limited to the ±1 × RMS and ±2 × RMS ranges around the centre of the distribution are shown, together with the respective differences between the ﬁtted functions and the actual distribution 
the widths measured by ﬁtting Gauss functions in selected ranges of the distribution. 
The tails in this shape are reﬂected in the distribution of Emiss 
T itself and can be estimated by measuring the fraction Ϭ Emiss threshold 
of events with ETmiss T , 
1 ∞
1 hEmiss dEmiss
ftail = TT
HEmiss threshold 
T 

1 
∞ 
hEmiss dEmiss
with H = (13)
TT 
0 
Here hEmiss is the Emiss distribution for a given event sam-
TT 
ple, and Emiss threshold 
T is a threshold set to estimate tails. Any decrease of ftail at a ﬁxed integral H indicates an improvement of the Emiss resolution, and is more sensitive to particu-
T lar improvements than e.g. RMSmiss. For example, improving 
xy the Emiss soft 
T reconstruction by rejecting ID tracks from the hard-scatter vertex with poor reconstruction quality yields a signiﬁcantly smaller ftail for the same event sample. 
The tails in the ETmiss distributions for the ﬁnal states considered for this study are quantiﬁed by the fraction of events above a certain Emiss threshold using MC simulations. Fig-
T ure 11a shows that the Z → λ events (λ = e or λ = ) with Emiss true 
T = 0 have signiﬁcantly reduced tails when compared to W → υ and tt¯ with this metric, and that the tails do not depend on the lepton ﬂavour. A modiﬁcation of this metric, taking into account Emiss true such that the fraction 
T 
− Emiss true 
of events with |Emiss | above a given threshold is 
TT determined, shows the universality of the hadronic recoil in Z → λ and W → , as can be seen in Fig. 11b. Another ﬁnding of this study is that the tail in the |Emiss −
T ETmiss true | distribution for the higher L ETjet tt¯sample is con
siderably larger than for the low-L Ejet samples with Z → 
T or W → υ ﬁnal states. As can be seen in Fig. 11c, the tails are much more consistent between Z → ͣ and tt¯samples when the distribution for the Z → ͣ sample is reweighted such that it follows the same L Ejet distribution as the tt¯
T sample. The enhanced tails are thus likely introduced by the jet response and multiplicity, which has a residual sensitivity to pile-up. 
7 Systematic uncertainties 
The systematic uncertainties associated with the measurement of Emiss are provided for the response (Emiss scale) as 
TT well as for the resolution. They depend on the composition of the hard terms and on the magnitude of the corresponding soft term. As the hard-term composition is generally deﬁned by optimisations implemented in the context of a given analysis, the contributions of the Emiss terms need to be extracted 
T from the scale and resolution uncertainties for the individual contributing objects comprising electrons, photons, muons, τ -leptons, and jets. In the corresponding propagations, cor
123 



T tions of Z → , W → ,and tt¯ﬁnal states. The tail fraction in terms 
− Emiss true 
of a threshold applied to |Emiss |, the distance between the 
TT reconstructed (Emiss) and the expected (Emiss true ) vectors, is shown in 
TT 
relations between systematic uncertainties for the same type of object are typically taken into account. However, it is assumed that systematic uncertainties of the different object types entering Emiss reconstruction are uncorrelated. The 
T determination of the ETmiss scale and resolution uncertain
ties arising from the soft term Emiss soft is described in this 
T section. 
bfor all considered ﬁnal states. The same fraction is shown in c for the Emiss 
T distributions for Z → ͣ before and after a reweighting following the L ET distribution for tt¯is applied, together with ftail from the tt¯ﬁnal state 
7.1 Methodology 
The extraction of the systematic uncertainties for the reconstructed Emiss is based on data-to-MC comparisons of spectra 
T of observables measuring the contribution of Emiss soft to the 
T overall ETmiss. 
123 


7.1.1 Observables 
The vector sum of the transverse momentum vectors of all particles and jets emerging from a hard-scatter interaction (pHS)isgiven by 
T 
HS e ͣ jet
p= ppppp
TT + T + T + T + T 
pT  (observable)  
+  pυ T  
inv  

obs 
p(not observable) 
T 
Here pυ T generally represents the transverse momenta of noninv 
observable particles, which are summed up to form pT .All 
other transverse momenta are carried by particles that are obs
observable in principle, and sum up to pT . Momentum con-HS HS
servation dictates p=|p|=0.
TT Due to detector acceptance limitations and inefﬁcien
cies in hard-object reconstruction and calibration, and all hard
other effects discussed in Sect. 3, only a proxy (pT )for the observable-particle contribution pobs can be measured. 
T The reconstructed hard ﬁnal-state objects entering Emiss as
T described in Sect. 3.2 are used to measure phard as
T 
hard e had
p= pT + pT + p
TT contributing contributing contributing electrons photons τ -leptons 
ͣ jet
+ pT + p
T contributing contributing 
muons jets 
hard hard hard obs
The expectation is that p=|p|Ϭ 0 and p =p.
TT TT 
=−Emiss soft , with Emiss soft 
Adding psoft deﬁned in Eq. (6),
TT T 
hard
to pT yields an improved estimate of the net transverse momentum carried by undetectable particles, as some of the experimental inefﬁciencies are mitigated.8 
In the Z →ͣ ﬁnal state without genuine missing trans-hard
verse momentum the expectation is that EmissT =−p+
T 
soft
pT =0. While this expectation does not hold due to the 
experimental inefﬁciencies, it nevertheless raises the expecsoft
tation that for events without jets pT points into the direction of the hadronic recoil, i.e. opposite to phard in the transverse-
T momentum plane. The deviation from this expectation is 
measured in terms of the parallel (Pf) and perpendicular soft hard
(P⊥) projections of pT onto pT . Figure 12 schematically shows these projections for Z+0-jet and Z+1-jet topologies. The average (Pf)in a given bin kof phase space deﬁned by 
hard hard
pT measures the Emiss soft response, with (Pf)=(pT )k
T indicating a perfect response in this bin. The Emiss resolution
T 
8 As discussed in Sect. 3.4, the soft term represents only charged parti<BR>
cles with pT Ϭ 400 MeV not associated with fully identiﬁed and reconsoft
structed particles or jets. Therefore, including pT can only recover a part of the actual soft pT-ﬂow of the interaction. 
contribution from Emiss soft reconstruction is measured by 
T two components, the ﬂuctuations in response (RMSf2) and 
the ﬂuctuations of the (transverse) angular deﬂection around hard
the paxis, measured by RMS⊥2 . These ﬂuctuations are 
T expressed in terms of variances, with 
    
��2
22
RMSf 2 =Pf− Pf and RMS⊥ 2 =P⊥ 
7.1.2 Procedures 
The extraction of the systematic uncertainties introduced into the Emiss 
measurement by the Emiss soft term is based on 
TT hard
data-to-MC-simulations comparisons of (Pf)( pfor the 
T 
hard hard
response, and of RMSf2( pand RMS⊥2 ( pfor the res-
TT olution. Alternative MC samples are considered, with variations of either the event generator or the detector simulation 
(description and shower models). For the highest impact of Emiss soft on Emiss 
T T , the exclusive Z → ͣ selection with Njet = 0 is the basis for the determination of the systematic uncertainty components for both data and all MC simulations. In this case, the only hard contribution is from 
hard Z
the reconstructed Z boson, i.e. pT = pT as shown in Fig. 12a. 
The uncertainties are determined by comparing Pf and P⊥ spectra from data and MC simulations, in bins of phard.
T For Pf, the smearing of the response and the width both yield scale and (longitudinal) resolution offsets. In the case of P⊥, only smearing of the width is applied to provide trans-verse resolution offsets. These ﬁtted offsets, determined for the various MC conﬁgurations, provide the systematic uncertainties with respect to a speciﬁc MC modelling conﬁguration. In practice, to account for the resolution offsets, Gaus-sian smearing is applied in simulation to the longitudinal and transverse components of Emiss soft relative to the direction 
T 
hard
of pT . To account for differences in response between data and simulation, the longitudinal component of Emiss soft is
T scaled up and down to give an uncertainty band. In order to generate the required number of simulated events, some analyses in ATLAS may have to use the fast detector simulation ATLFAST2 [38,45] for the calorimeter response. It employs parameterisations for electromagnetic and hadronic showers, instead of the explicit simulation of the particle tracking through matter and the energy-loss mechanisms in a detailed detector geometry. An additional uncertainty is assigned to effects introduced by ATLFAST2.This uncertainty contribution only needs to be considered in analyses using this fast simulation, and does not apply for the results presented in this paper. In analyses where it is applicable, it is added in quadrature to the standard uncertainties. 
123 




(b) 


soft hard
projections of pon pfor Z →ͣ events without genuine Emiss,
TT T 
for a a ﬁnal state without any jets and ba ﬁnal state with one jet. The 
7.2 Systematic uncertainties in Emiss response and 
T 
resolution 
Emiss
The result for the systematic uncertainty of the T scale, determined as discussed in the previous section, is summarised in Fig. 13. The average longitudinal projec
soft hard hard
tion of ponto p, (Pf), as a function of pis
TT T 
shown in Fig. 13a which compares data to both the stan<BR>dard Powheg+Pythia8-based simulations and the alternative MC simulation employing MadGraph, as described in Sect. 4.2. All MC simulation results are expected to have (Pf)MC within the uncertainties of the data. The lower panel of Fig. 13a conﬁrms that the ratio (Pf)MC(Pf)data lies within the systematic uncertainty band over the full phard 
T 
range. The systematic uncertainty for the Emiss resolution is 
T extracted from the variances of the parallel (RMSf2) and per-
hard
pendicular (RMS⊥2 ) projections of Emiss onto pdeﬁned in 
TT 
hard
Sect. 7.1.2. Figure 13bshows the pdependence of RMS2 
T f 
measured for the exclusive Z → ͣ sample (Njet = 0) in data and two MC simulations. The variances RMSf 2 MC calculated for both sets of simulations agree within the systematic uncertainties of RMSf 2 data with the data, as illustrated in the lower panel of the ﬁgure, where the ratio 
hard
RMSf 2 MCRMSf 2 data is shown as a function of p.The 
T 


Z
expectation values for a perfect Emiss reconstruction are E[Pf]=p
TT 
hard
for Njet =0and E[Pf]=pfor Njet ↖1, with E[P⊥]=0in all 
T cases. a Z +0 jet topology. b Z +1 jet topology 
results of the evaluation of the variances RMS⊥2 of the perpendicular projections as a function of pThard are shown in Fig. 13c, together with the resulting pThard dependence of the ratio RMS⊥ 2 MCRMS⊥ 2 data. The systematic uncertainties of the data cover all differences to MC simulations. 
8Missing transversemomentumreconstruction variants 
8.1 Calorimeter-based ETmiss 
The Emiss 
T soft term from the calorimeter ETmiss soft calo is reconstructed from topo-clusters. As discussed in Ref. [6], each topo-cluster provides a basic EM scale signal as well as a calibrated signal reconstructed using local cell weighting 
(LCW), and Emiss soft calo 
T is calculated from topo-clusters calibrated at the LCW scale. Only topo-clusters with a calibrated energy ELCW Ϭ 0, not contributing to the recon
clus struction of the hard objects used to calculate the hard term given in Eq. (6), are considered for Emiss soft calo . In addi-
T tion, topo-clusters that are formed at the same location as the 
hard object signals are not considered for Emiss soft calo even 
T if their signals are not directly contributing to the reconstruc
tion of the hard objects. The fully reconstructed Emiss using
T Emiss soft calo is Emiss calo 
T T. 
123 


24 
140
22 20 
120
18 16 

100 
MC/Data <P > [GeV] 
10 8 


40 2 

60 
6 
4 



RMS2 [GeV2] 

14 12 
80 

0.8 

0.8 102030405060708090100 102030405060708090100 
phard [GeV] phard [GeV]
(a) T(b) T
MC/Data
0 
20 
1.2 1.2 
1.1 1.1 
1 
1 
0.9 0.9 


102030 40 
the b variance RMS2 of the longitudinal projection Pf of ponto
f T phard for Z → ͣ event with Njet = 0, for data and two different 
T hard
MC simulations, shown as a function of pT . The variance RMS⊥ 2 of 
and L ET, Emiss calo 
Compared to the reference Emiss and
TT 
L Ecalo 
T have an enhanced dependence on pile-up, mostly introduced by the soft term. To partly compensate for the irreducible contribution of pT-ﬂow reconstructed from topoclusters generated by pile-up to Emiss calo , a modiﬁed jet 
T selection and ambiguity resolution is applied in their reconstruction. The considered jets are reconstructed following the prescription in Sect. 3.3.5, and required to have a fully calibrated pT Ϭ 20 GeV. The contribution of these jets to Emiss calo and L Ecalo 
T T , deﬁned in terms of momentum compo

50 60 70 80 90100 
phard [GeV]

(c) T
the perpendicular projection P⊥ is shown in c for the same event samples. The shaded band indicates the systematic uncertainties derived as described in the text 
nents ( px py , depends on the overlap with already accepted reconstructed particles, 
00 E ↖ 50% (large overlap) 
( px py = jet jet

1 − E × ( px py E Ϫ 50% (small or no overlap) 
(14) 
The overlap fraction E is given in Eq. (8). Jets with E ↖ 50% are not used at all. The JVT-based tagging of non-pileup jets is omitted. It is found that this strategy reduces the ﬂuctuations in the ETmiss calo reconstruction. The transverse 
123 


momentum contribution of groups of clusters representing a jet-like pT-ﬂow e.g. from pile-up in a given direction that are not reconstructed and calibrated as a jet, or do not pass the jet-pT threshold applied in Emiss reconstruction, is reduced 
T if all jets and jet fragments, including those from pile-up, are included. 
8.2 Emiss 
from tracks 
T 
The reference track-based soft term Emiss soft is largely insen-
T sitive to pile-up, as indicated by the dependence of the Emiss 
T resolution RMSmiss on NPV in the exclusive Z → ͣ sam
xy ple (Njet = 0) shown in Fig. 7c. As discussed in Sect. 6.2.4 and from the comparison of Fig. 7c, d, the pile-up depen<BR>dence of RMSmiss in the inclusive Z → ͣ sample is largely 
xy 
introduced by the jet contribution. This contribution suffers from (1) the lack of pile-up suppression for forward jets with || Ϭ 2 4, (2) any inefﬁciency connected with the JVT-based tagging, and (3) irreducible pile-up-induced ﬂuctuations in the calorimeter jet signals. Using a representation of Emiss 
T employing only reconstructed ID tracks from the primary vertex increases stability against pile-up as long as the tracking and vertex resolution is not affected by it. In this representation ( pmiss) all jets and reconstructed particles are ignored, 
T 
i.e. the pTmiss reconstruction does not include any calorimeter or MS signals. The pmiss resolution is then inherently 
T immune to pile-up, while the pTmiss response is low as all neutral pT-ﬂow in || Ϫ 2 5aswellasall pT-ﬂow outside of this region is excluded. 
8.3 Performance evaluations for ETmiss variants 
The main motivation to study Emiss-reconstruction variants 
T is to improve some combination of the Emiss resolution,
T scale, and stability against pile-up. As with the composition 
of objects entering Emiss reconstruction in general, the par-
T ticular choice of variant used for a given analysis strongly 
depends on the performance requirements for this analysis. The comparison of both the resolution and response of Emiss calo miss 
T and pT to the corresponding measurements using the reference Emiss illustrates their principal features for the 
T Z → ͣ and tt¯production ﬁnal state. 
8.3.1 Comparisons ofE miss resolution 
T miss
Figure 14 compares the Emiss calo and presolutions with 
TT the one obtained from the reference Emiss, for the inclusive 
T Z → ͣ sample in data. Each is shown as a function of L ET corresponding to the reference Emiss, giving an esti-
T mate of the total hard-scatter activity. The low-L ET region 
is dominated by events with Njet = 0, where the contribution of Emiss soft calo in Emiss calo 
T T yields a poorer resolution than 

for Emiss miss
, and where Emiss and phave identical perfor-
T TT mance. The high-L ET region is dominated by events with 
higher jet multiplicity, where pmiss resolution is degraded rel-
T ative to the reference Emiss by the incomplete measurement 
T of jets. miss
Figure 15a compares the Emiss calo and presolution as 
TT functions of the pile-up activity measured by NPV, with the one obtained from the reference ETmiss for the exclusive Z → ͣ samples with Njet = 0 in data. The Emiss calo resolution
T is dominated by pile-up and shows signiﬁcantly degraded miss
performance relative to pand the reference Emiss.The 
TT exclusive use of only tracks from the hard-scatter vertex for miss and Emiss
both pT T yields the same stability against pile-up. 
In events with jet activity, the degraded pTmiss resolution is observable, especially outside the region of high-est pile-up activity, as seen in Fig. 15bfor the Emiss res-
T olution obtained with the inclusive Z → ͣ sample in data for NPV 15. This is even more obvious in ﬁnal states with relatively high jet multiplicity and genuine missing transverse momentum, like for the tt¯-production sam
ple from MC simulations. As shown in Fig. 15c for this ﬁnal state, both the reference ETmiss and the calorimeterbased Emiss calo 
T have a signiﬁcantly better resolution than pmiss, at the price of some sensitivity to pile-up, which is 
T absent for pmiss.The NPV dependence of the resolution is 
T enhanced in Emiss calo , due to the increased contribution from 
T soft calorimeter signals without pile-up suppression at higher 
NPV. 
123 



T 
olutions of the track-only-based variant pmiss described in Sect. 8.2,
T and the reconstruction variant Emiss calo employing a calorimeter-based 
T 
soft term, as discussed in Sect. 8.1. The resolutions are determined as described in Sect. 6.2.3 and shown as a function of the pile-up activity 
8.3.2 Comparisons ofE miss scale
T 
Following the description in Sect. 6.2.1,the Emiss response is 
T 
, Emiss calo miss
evaluated for the reference Emiss , and pusing
TT T 
, Emiss calo miss
the respective projections of Emiss , and ponto
TT T 
the direction of pT Z, according to Eqs. (9) and (10). Fig-
Z
ure 16a shows the average projection as a function of pT for the exclusive Z → ͣ sample with Njet = 0 in data. 
miss
Both Emiss and pshow the same increasingly incomplete 
TT 
reconstruction of the hadronic recoil in this sample for rising 
measured in terms of the number of reconstructed vertices NPV for a an exclusive Z → ͣ sample without jets with pT Ϭ 20 GeV and ban inclusive Z → ͣ sample, both selected from data. In c, the resolution of the Emiss reconstruction-variants in a ﬁnal state with signiﬁcant jet 
T 

activity and pT Ϭ 0 is compared using MC simulations of tt¯production 
Z
pT . This reconstruction is slightly improved for Emiss calo ,
T 
but still insufﬁcient at higher pT Z . In the inclusive Z → ͣ sample, shown in Fig. 16b, the 
T is that Emiss calo 
indication at lower pZ has a higher response 
T 
and thus a better representation of the hadronic recoil, due to the more complete Emiss soft reconstruction and the lack 
T 
of a JVT-tagging requirement. This effect is partly due to the observation bias in the response introduced by the relatively poor Emiss calo resolution, as discussed in Sect. 3.1.
T 
and Emiss calo 
Both ETmiss T show comparable response for 
123 



T 
Emiss calo 	miss 
T and track-only-based pT response in an a exclusive and 
an b inclusive 	Z → ͣ sample from data. The projections of the , Emiss calo miss Z
respective Emiss ,and ponto the direction of pT , calcu-
TT T 
Z
pT 2 60 GeV, owing to the JVT cut-off at 60 GeV. The slightly larger Emiss calo response of about 1 GeV reﬂects 
T the contribution from neutral signals to the soft term. The 
degraded response associated with pTmiss related to the exclu
sion of hard objects is clearly visible in this ﬁgure. Emiss
Figure 16c shows the linearity of the various T reconstruction approaches as a function of Emiss true for
T the tt¯-production sample from MC simulations. Beyond Emiss true and Emiss calo 
≈ 60 GeV both the reference Emiss 
T TT show the same good linearity, while the lack of a jet con
lated according to Eqs. (9)and (10), are shown as a function of pT Z.In c, , Emiss calo miss
the linearity of the reference ETmiss T ,and pT scales, calculated according to Eq. (11), is shown as a function of the true Emiss true 
T for the tt¯-production MC simulation sample 
tribution to pmiss shows a loss of response up to about 50% 
T at higher Emiss true . The overestimation of Emiss true by all 
TT three reconstruction variants at lower ETmiss true reﬂects the observation bias in the response introduced by the resolution. The poorer resolution associated with pTmiss observed in Fig. 15c for this sample leads to a faster rise of the response with decreasing Emiss true than for the reference 
T 
Emiss and Emiss calo 
T T , which show a very similar dependence on Emiss true 
T. 
123 


8.3.3 Summary of performance 
Both Emiss calo miss 
T and pT offer alternative measures for 
Emiss 
. The calorimeter-based Emiss calo uses topo-clusters 
TT 
calibrated at the LCW scale for the soft term, which are neither part of the signal nor otherwise overlapping with the signals of other hard objects contributing to Emiss.This 
T introduces a pile-up dependence into Emiss calo , due to the 
T 
lack of pile-up suppression of calorimeter signals outside of reconstructed hard objects. It features a slightly modiﬁed jet contribution without the JVT-based selection used in case of the reference Emiss reconstruction, to allow the cancel-
T lation of jet-like pT-ﬂow from pile-up in Emiss soft calo by
T pile-up jets in its hard term. The Emiss calo response in the 
T 
inclusive Z → ͣ sample is better than the reference Emiss 
T 
response, in particular in the region of small hadronic recoil 
Z
( pT 20 GeV). It is comparable to the reference in tt¯ ﬁnal states. The observed RMSmiss, in particular in Z → 
xy 
without jets, is signiﬁcantly more affected by pile-up than 
miss
is the reference Emiss or the track-only-based p. In ﬁnal 
TT 
t, Emiss calo 
states with a considerable number of jets, like t¯T performs nearly as well as the reference Emiss, with a slight 
T 
degradation of the Emiss resolution at highest pile-up activi-
T 
ties. This variant is useful for physics analyses least sensitive to the soft-term contribution to Emiss resolution but requiring 
T 
a linear Emiss response.
T 
The track-only-based pmiss displays a degraded response 
T 
for the inclusive Z → ͣ sample, which is expected from the exclusive use of hard-scatter-vertex tracks. As expected, resolution is not affected by pile-up in the considered ﬁnal states, but is poorer than, or at most comparable to, the refer
miss
ence Emiss algorithm. Nevertheless, pprovides a stable 
TT 
observable for event and phase-space selections in analyses sensitive to ETmiss resolution. 
9 Conclusion 
The performance and features of the missing transverse momentum reconstruction in pp collision data at the LHC, 
√ 
acquired in 2015 with the ATLAS detector at s = 13 TeV and corresponding to about 3 2fb−1, are evaluated for selected event samples with (W → e, W → , tt¯) and without (Z → ) genuine Emiss. The comparison 
T 
of the data from the detector with the corresponding MC simulations generally yields good agreement in the covered phase space. The systematic uncertainty contribution from the soft event to the reconstructed ETmiss is determined with Z → ͣ ﬁnal states without jets. It is calculated from the data-to-MC-simulations comparison of the parallel and perpendicular projections of the missing transverse momentum vector EmissT onto the vector sum of the transverse momenta of the hard objects phard. The parallel projections yield the 
T uncertainty of the Emiss scale, evaluated as a function of the 
T total transverse momentum of the hard objects (pThard). The 
widths of the distributions of the parallel and perpendicular 
projections yield the respective systematic uncertainties of the Emiss 
resolution. Simulation tends to underestimate the 
T perpendicular resolution and overestimate the scale and par-allel resolution, in each case differing from data by at most 10%. The performance evaluation of Emiss response and res-
T olution for the inclusive Z → ͣ sample shows that data and MC simulations agree within the systematic uncertainties. The Emiss response shows an underestimation of the soft 
T contributions to Emiss. A degradation of the Emiss resolution
TT is observed for increasing L ET and NPV, due to pile-up and 
detector resolution effects. Additional performance measures 
considered in these studies include the estimate of tails in the Emiss 
T distribution. As expected from the universality of the hadronic recoil, the integral tail fraction of the Emiss distri-
T bution is identical for inclusive Z and W boson production, independent of the leptonic decay mode. The tt¯ ﬁnal states feature a higher jet multiplicity and show larger tails reﬂecting a higher sensitivity to residual pile-up surviving in the jet contribution to Emiss, in terms of the inclusion of pile-up 
T jets as well as the increased ﬂuctuations of the jet response introduced by pile-up. From the performance studies presented in this paper, the object-based Emiss reconstruction in ATLAS, which was 
T developed for LHC Run 1 and used in a large number of physics analyses, can be used with the discussed reﬁnements and adjustments for Run 2 as well. 
Acknowledgements We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efﬁciently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS, CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, the Canada Council, CANARIE, CRC, Compute Canada, FQRNT, and the Ontario Innovation Trust, Canada; EPLANET, ERC, ERDF, FP7, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d’Avenir Labex and Idex, ANR, Région Auvergne and Fondation Partager le Savoir, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-ﬁnanced by 
123 


EU-ESF and the Greek NSRF; BSF, GIF and Minerva, Israel; BRF, Norway; CERCA Programme Generalitat de Catalunya, Generalitat Valenciana, Spain; the Royal Society and Leverhulme Trust, United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in Ref. [46]. 
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecomm ons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Funded by SCOAP3. 
AppendixA:Glossaryof terms 
In this paper several acronyms and qualiﬁers are used to describe the reconstruction of ETmiss and related observables. This brief glossary of terms is intended to help with the nomenclature. All terms should be interpreted in the context 
of Emiss 
T reconstruction and may have other interpretations in other contexts. 
Emiss 
T reconstruction: Using this nomenclature usually encompasses the reconstruction of a set of observables comprising the missing transverse momentum vector 
Emiss 
T , its components Exmiss y , and its absolute value ETmiss. In addition, the scalar sum L ET of the pT of all kinematic objects contributing to Emiss is calculated. The calcula-
T tion of these variables is described in detail in Eqs. (1)–(4) in Sect. 3.1. Hard scatter and primary vertices: The hardest pp interaction in a given event is referred to as the hard scatter. It is normally associated with a reconstructed hard-scatter vertex, which is considered the hardest vertex among all reconstructed primary vertices in this event. The hard-scatter vertex is deﬁned as the one with the largest sum of pT2 of tracks associated with it. The other primary vertices are assumed to be produced by in-time pile-up interactions. The variable NPV denotes the number of reconstructed primary collision vertices in the event. 
Hard event and hard term: The reconstruction of hard objects includes individual particles such as electrons, photons, muons and τ -leptons, and jets. In all cases the ﬁnal objects are characterised by a kinematic threshold and reconstruction quality requirements. Both the reconstructed charged-particle tracks from the ID and topo-clusters from the calorimeter are used as the input signals for these objects. In the context 
Emiss
of T reconstruction, the use of the same detector signals by different hard objects is excluded, see details in Sect. 3. The ﬁnally accepted hard event objects give rise to the hard term in Emiss reconstruc-
T tion. Soft event and soft term: All detector signals recorded for one triggered event and not used by the hard objects discussed above can be considered as soft signals contributing to Emiss. They include signals or signal traces 
T from scattered soft particles arising from the underlying event accompanying the hard-scatter interaction, or from statistically completely independent pile-up interactions producing diffuse particle emissions in the same bunch crossing. In addition, signals from particles and jets which do not satisfy the hard-object quality criteria, or are below the kinematic threshold, can be included in the soft event. The reference ETmiss reconstruction conﬁguration for the results presented in Sects. 6 and 7 uses reconstructed ID tracks from the soft event to form the soft term in ETmiss reconstruction, with the track selection details outlined under priority (6) in Table 1 in Sect. 3. Alternative conﬁgurations employ topo-clusters from the calorimeter, see Table 2 in Appendix A. 
AppendixB:Alternative Emiss composition
T 
Table 2 summarises the Emiss reconstruction conﬁgurations 
T employing only ID tracks, or using topo-clusters for the soft 
term. 
123 


Table2 Representations of Emiss and L ET calculated from (1) reconstructed charged-particle tracks from the ID or (2) using a soft term from 
T 
topo-clusters in the calorimeter only 
# Objects contributing to Emiss and L ET
T 

Type Selections 	Variables Comments 
miss

(1) 	ID track || Ϫ 25 ppT Ϭ 400 MeV T pT 
|d0| Ϫ 1 5mm |z0 sinω | Ϫ 1 5mm 

Emiss soft calo 
ELCW

(2) Topo-cluster >0(softterm) T
clus L Esoft calo T 
Charged-particle-based estimators for Emiss,
T L ET using all reconstructed tracks from the hard-scatter vertex passing requirements for high-quality reconstruction in addition to kinematic selections 
(L Ecalo
Variant reconstructing Emiss calo )
TT (L Esoft calo 
using a soft term Emiss soft calo )
TT reconstructed from topo-clusters not used by, or not overlapping with, the hard objects used for the hard term composed of items (1)–(5) in Table 1, with the jet selection described in Sect. 8.1 applied 
AppendixC:Jet selection 
As discussed in Sect. 3.3.5, jets that are not rejected by the sig<BR>
nal ambiguity resolution and have pT Ϭ 60 GeV contribute to Emiss 
reconstruction. Jets with less transverse momentum 
T that fall within || Ϫ 2 4 are subjected to further selection based on JVT calculated by a track-based jet vertex tagger [16]. Three values for JVT, each representing a different efﬁciency for the reconstruction of non-pile-up jets, were considered in the course of the optimisation of the JVT-based selection, 
JVTtight Ϭ 0 11 tight selection with high pile-up 
rejection power at lower signal efﬁciency; 
JVTmedium Ϭ 0 59 medium selection with good signal efﬁciency and pile-up rejection power; 
JVTloose Ϭ 0 91 loose selection with lower pile-up 
rejection power and higher signal efﬁciency. 

Emiss
The effects of these selections 	on the resolution
T 
RMSmiss 
xy and response are shown in Fig. 17,for Z → ͣ events in MC simulation. Figure 17a shows that the pile-up dependence of RMSmiss is not signiﬁcantly affected by 
xy the choice for JVT. However, the Emiss response mea-
T sured by the projection given in Eq. (10) in Sect. 6.2.1 
Z
and evaluated as a function of pT in the same sample, shows signiﬁcant sensitivity to the choice of JVT, as seen in Fig. 17b. 
In addition to the signal ambiguity resolution and the choice for JVT, the contribution from jets in Emiss recon-
T struction is controlled by a kinematic threshold requiring the 
transverse momentum of the jet to be pT Ϭ 20 GeV. The effects of variations of this threshold on RMSmiss and the 
xy 
Emiss 
T response are shown in Fig. 18. Increasing the thresh<BR>old to 30 GeV for all jets satisfying the JVTmedium condition reduces the pile-up dependence of the resolution shown in Fig. 17a, but leads to signiﬁcant loss of Emiss response, as 
T 
seen in Fig. 17b. Depending on the sensitivities observed in a given physics analysis, the pT-threshold choice for the jet contribution to Emiss reconstruction needs to be adjusted to 
T 
meet the required performance. 
Extending the pT threshold studies with the option of regional thresholds yields the performance results presented in Fig. 19. In this case jets within || Ϫ 2 4 are sub-
central jet 
jected to the pT Ϭ 20 GeV selection, while jets 
forward jet 
outside of this χ range are ﬁltered using pT Ϭ {20 2530} GeV. This leads to the improvements in the pile-up dependence of RMSmiss shown in Fig. 19a, which are 
xy 
very similar to the ones observed in Fig. 18a for a global jet-pT threshold variation. The comparison indicates that the main pile-up contribution to RMSmiss is introduced by for
xy 
ward jets, for which no JVT-based pile-up-mitigation is available. 
Increasing the pT threshold only for forward jets reduces the average loss of response observed in case of the global pT threshold increase. This can be seen by comparing the results shown in Fig. 19b for regional pT thresholds with the ones shown in Fig. 18b for global thresholds. Like for the JVT threshold selection, the choice of the appropriate global or regional pT-threshold depends on the Emiss reconstruc-
T 
tion performance required in the context of a given analysis. 
123 



Emiss Z
T discussed in the text, as measured for Z →ͣ events in MC simulation. global selection of pT Ϭ 20 GeV is applied to the transverse momentum The a Emiss central jet 
T resolution is shown as function of the pile-up activity mea-of jets within ||Ϫ 24(p) and for forward jets with ||↖24 
T sured by the number of primary vertices NPV,and the bEmiss response forward jet 
T(pT) 

Emiss Z
T central jet forward jet 

shown as function of the pile-up activity measured by the number of ||Ϫ 24(p) and for forward jets with ||↖24(p),
TT primary vertices NPV,and the b ETmiss response is shown as function with pcentral jet pforward jet Ϭ {20 2530}GeV 
TT 
123 



Emiss
T 
shown as function of the pile-up activity measured by the number of primary vertices NPV,and the b Emiss response is shown as function 
T 
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ATLASCollaboration 

M. Aaboud34d, G. Aad99, B. Abbott125, O. Abdinov13,*, B. Abeloos129, S. H. Abidi165, O. S. AbouZeid143, 
N. L. Abraham153, H. Abramowicz159, H. Abreu158, R. Abreu128, Y. Abulaiti43a,43b, B. S. Acharya64a,64b,p, 
S. Adachi161, L. Adamczyk81a, J. Adelman119, M. Adersberger112, T. Adye141, A. A. Affolder143, Y. Aﬁk158, 
T. Agatonovic-Jovin16, C. Agheorghiesei27c, J. A. Aguilar-Saavedra137a,137f,aj, F. Ahmadov77,ah, G. Aielli71a,71b, 
S. Akatsuka83, H. Akerstedt43a,43b, T.P.A.Åkesson94, E. Akilli52, A. V. Akimov108, G. L. Alberghi23b,23a, 
J. Albert174, P. Albicocco49, M. J. Alconada Verzini86, S. Alderweireldt117, M. Aleksa35, I. N. Aleksandrov77, 
C. Alexa27b, G. Alexander159, T. Alexopoulos10, M. Alhroob125,B. Ali139, G. Alimonti66a, J. Alison36, S. P. Alkire38, 
B. M. M. Allbrooke153, B.W.Allen128, P.P.Allport21, A. Aloisio67a,67b, A. Alonso39, F. Alonso86, C. Alpigiani145, 
A. A. Alshehri55, M. I. Alstaty99, B. Alvarez Gonzalez35, D. Álvarez Piqueras172, M.G.Alviggi67a,67b, B.T.Amadio18, 
Y. Amaral Coutinho78b, C. Amelung26, D. Amidei103, S. P. Amor Dos Santos137a,137c, S. Amoroso35, G. Amundsen26, 
C. Anastopoulos146, L. S. Ancu52, N. Andari21, T. Andeen11, C. F. Anders59b, J. K. Anders88, K. J. Anderson36, 
A. Andreazza66a,66b, V. Andrei59a, S. Angelidakis37, I. Angelozzi118, A. Angerami38, A. V. Anisenkov120b,120a, 
N. Anjos14, A. Annovi69a, C. Antel59a, M. Antonelli49, A. Antonov110,*, D.J.A.Antrim169, F. Anulli70a, M. Aoki79, 
L. Aperio Bella35, G. Arabidze104,Y. Arai79, J. P. Araque137a, V. Araujo Ferraz78b, A.T.H.Arce47, R. E. Ardell91, 
F. A. Arduh86, J-F. Arguin107, S. Argyropoulos75,M. Arik12c, A. J. Armbruster35, L. J. Armitage90, O. Arnaez165, 
H. Arnold50, M. Arratia31,O. Arslan24, A. Artamonov109,*, G. Artoni132,S. Artz97,S. Asai161, N. Asbah44, 
A. Ashkenazi159, L. Asquith153, K. Assamagan29, R. Astalos28a, M. Atkinson171, N.B.Atlay148, K. Augsten139, 
G. Avolio35,B. Axen18, M. K. Ayoub129, G. Azuelos107,ax, A. E. Baas59a, M.J.Baca21, H. Bachacou142, K. Bachas65a,65b, 
M. Backes132, P. Bagnaia70a,70b, M. Bahmani82, H. Bahrasemani149, J. T. Baines141, M. Bajic39, O.K.Baker181, 
E. M. Baldin120b,120a, P. Balek178, F. Balli142, W. K. Balunas134, E. Banas82, A. Bandyopadhyay24, S. Banerjee179,m, 
A. A. E. Bannoura180, L. Barak159, E. L. Barberio102, D. Barberis53b,53a, M. Barbero99, T. Barillari113, M-S. Barisits35, 
J. Barkeloo128, T. Barklow150,N. Barlow31, S. L. Barnes58c, B. M. Barnett141, R. M. Barnett18, Z. Barnovska-Blenessy58a, 
123 


A. Baroncelli72a, G. Barone26,A. J. Barr132, L. Barranco Navarro172,F. Barreiro96, J. Barreiro Guimarães da Costa15a, R. Bartoldus150, A. E. Barton87, P. Bartos28a, A. Basalaev135, A. Bassalat129, R. L. Bates55, S. J. Batista165, J. R. Batley31, M. Battaglia143, M. Bauce70a,70b, F. Bauer142,H. S. Bawa150,n, J. B. Beacham123, M. D. Beattie87, T. Beau133, P. H. Beauchemin168, P. Bechtle24, H. C. Beck51, H. P. Beck20,t, K. Becker132, M. Becker97, C. Becot122, A. Beddall12d, A. J. Beddall12a, V. A. Bednyakov77, M. Bedognetti118,C. P. Bee152, T. A. Beermann35, M. Begalli78b, M. Begel29, J.K.Behr44,A. S. Bell92, G. Bella159, L. Bellagamba23b, A. Bellerive33, M. Bellomo158, K. Belotskiy110, O. Beltramello35, N. L. Belyaev110, O. Benary159,*, D. Benchekroun34a, M. Bender112, K. Bendtz43a,43b, N. Benekos10, Y. Benhammou159, E. Benhar Noccioli181, J. Benitez75, D. P. Benjamin47, M. Benoit52, J. R. Bensinger26, S. Bentvelsen118, L. Beresford132, M. Beretta49, D. Berge118, E. Bergeaas Kuutmann170, N. Berger5, J. Beringer18, S. Berlendis56, N. R. Bernard100,G. Bernardi133,C. Bernius150,F. U. Bernlochner24,T. Berry91,P. Berta97,C. Bertella15a,G. Bertoli43a,43b, F. Bertolucci69a,69b, I. A. Bertram87, C. Bertsche44, D. Bertsche125, G.J.Besjes39, O. Bessidskaia Bylund43a,43b, M. Bessner44, N. Besson142, A. Bethani98, S. Bethke113, A.J.Bevan90, J. Beyer113, R. M. Bianchi136, O. Biebel112, D. Biedermann19, R. Bielski98, K. Bierwagen97,N. V. Biesuz69a,69b, M. Biglietti72a, T.R.V.Billoud107, H. Bilokon49, M. Bindi51, A. Bingul12d,C. Bini70a,70b, S. Biondi23b,23a, T. Bisanz51, C. Bittrich46, D. M. Bjergaard47, J. E. Black150, K. M. Black25, R. E. Blair6, T. Blazek28a, I. Bloch44, C. Blocker26,A. Blue55,W. Blum97,*, U. Blumenschein90, Dr. Blunier144a, G. J. Bobbink118, V. S. Bobrovnikov120b,120a, S. S. Bocchetta94, A. Bocci47, C. Bock112, M. Boehler50, D. Boerner180, D. Bogavac112, A. G. Bogdanchikov120b,120a, C. Bohm43a, V. Boisvert91, P. Bokan170,T. Bold81a, A. S. Boldyrev111,A. E. Bolz59b, M. Bomben133, M. Bona90, M. Boonekamp142,A. Borisov121, G. Borissov87, J. Bortfeldt35,D. Bortoletto132,V. Bortolotto61a,61b,61c,D. Boscherini23b,M. Bosman14,J.D.Bossio Sola30,J. Boudreau136, J. Bouffard2, E. V. Bouhova-Thacker87, D. Boumediene37, C. Bourdarios129, S.K.Boutle55, A. Boveia123,J. Boyd35, I. R. Boyko77, J. Bracinik21, A. Brandt8, G. Brandt51, O. Brandt59a, U. Bratzler162,B. Brau100, J.E.Brau128, W. D. Breaden Madden55, K. Brendlinger44, A. J. Brennan102, L. Brenner118, R. Brenner170, S. Bressler178, D. L. Briglin21, T. M. Bristow48, D. Britton55, D. Britzger44, I. Brock24, R. Brock104, G. Brooijmans38, T. Brooks91, W. K. Brooks144b, J. Brosamer18,E. Brost119, J. H Broughton21, P. A. Bruckman de Renstrom82, D. Bruncko28b, A. Bruni23b, G. Bruni23b, L. S. Bruni118, B.H. Brunt31, M. Bruschi23b,N. Bruscino24, P. Bryant36, L. Bryngemark44, T. Buanes17, Q. Buat149, P. Buchholz148, A. G. Buckley55, I. A. Budagov77, M. K. Bugge131, F. Bührer50, O. Bulekov110, D. Bullock8, T.J.Burch119, S. Burdin88,C. D. Burgard50, A. M. Burger5, B. Burghgrave119, K. Burka82,S. Burke141, I. Burmeister45,J. T. P. Burr132, E. Busato37, D. Büscher50, V. Büscher97, P. Bussey55, J.M.Butler25, C.M.Buttar55, J. M. Butterworth92, P. Butti35, W. Buttinger29, A. Buzatu155, A. R. Buzykaev120b,120a, S. Cabrera Urbán172, D. Caforio139, V.M.M.Cairo40a,40b, O. Cakir4a, N. Calace52, P. Calaﬁura18, A. Calandri99, G. Calderini133, P. Calfayan63, G. Callea40b,40a, L. P. Caloba78b, S. Calvente Lopez96,D. Calvet37,S. Calvet37,T. P. Calvet99, R. Camacho Toro36, S. Camarda35, P. Camarri71a,71b, D. Cameron131, R. Caminal Armadans171, C. Camincher56, S. Campana35, M. Campanelli92, A. Camplani66a,66b, A. Campoverde148, V. Canale67a,67b, M. Cano Bret58c, J. Cantero126, T. Cao159, M. D. M. Capeans Garrido35, I. Caprini27b, M. Caprini27b, M. Capua40b,40a, R. M. Carbone38, R. Cardarelli71a, F. C. Cardillo50, I. Carli140, T. Carli35, G. Carlino67a, B. T. Carlson136, L. Carminati66a,66b, R.M.D.Carney43a,43b, S. Caron117, E. Carquin144b, S. Carrá66a,66b, G. D. Carrillo-Montoya35, D. Casadei21, M. P. Casado14,g, M. Casolino14, D. W. Casper169, R. Castelijn118, V. Castillo Gimenez172,N. F. Castro137a, A. Catinaccio35,J. R. Catmore131, A. Cattai35, J. Caudron24, V. Cavaliere171, E. Cavallaro14,D. Cavalli66a,M. Cavalli-Sforza14,V. Cavasinni69a,69b,E. Celebi12b,F. Ceradini72a,72b,L. Cerda Alberich172, A. S. Cerqueira78a,A. Cerri153,L. Cerrito71a,71b,F. Cerutti18,A. Cervelli20,S. A. Cetin12b,A. Chafaq34a,D. Chakraborty119, S. K. Chan57, W. S. Chan118, Y. L. Chan61a, P. Chang171, J. D. Chapman31, D.G.Charlton21, C. C. Chau33, C. A. Chavez Barajas153,S. Che123, S. Cheatham64a,64c, A. Chegwidden104, S. Chekanov6, S. V. Chekulaev166a, G. A. Chelkov77,aw, M.A.Chelstowska35, C. H. Chen76, H. Chen29, J. Chen58a, S. Chen161, S.J.Chen15c, X. Chen15b,av, Y. Chen80, H. C. Cheng103, H. J. Cheng15d, A. Cheplakov77, E. Cheremushkina121, R. Cherkaoui El Moursli34e, E. Cheu7, K. Cheung62, L. Chevalier142, V. Chiarella49, G. Chiarelli69a, G. Chiodini65a, A. S. Chisholm35, A. Chitan27b, Y. H. Chiu174, M. V. Chizhov77, K. Choi63, A. R. Chomont37, S. Chouridou160,Y. S. Chow61a, V. Christodoulou92, M. C. Chu61a, J. Chudoba138, A. J. Chuinard101, J. J. Chwastowski82,L. Chytka127, A.K.Ciftci4a, D. Cinca45, V. Cindro89, I. A. Cioar˘a24, C. Ciocca23a,23b, A. Ciocio18, F. Cirotto67a,67b, Z. H. Citron178, M. Citterio66a, M. Ciubancan27b, A. Clark52,B. L. Clark57, M.R.Clark38,P. J. Clark48,R. N. Clarke18, C. Clement43a,43b, Y. Coadou99, M. Cobal64a,64c, A. Coccaro52, J. Cochran76, L. Colasurdo117,B. Cole38, A. P. Colijn118, J. Collot56, T. Colombo169, P. Conde Muiño137a,j, E. Coniavitis50, S. H. Connell32b, I. A. Connelly98, S. Constantinescu27b, G. Conti35, F. Conventi67a,ay, M. Cooke18, A. M. Cooper-Sarkar132, F. Cormier173, K.J.R.Cormier165, M. Corradi70a,70b, F. Corriveau101,af, A. Cortes-Gonzalez35, G. Cortiana113,G. Costa66a, M.J.Costa172, D. Costanzo146, G. Cottin31,G. Cowan91,B. E. Cox98, K. Cranmer122, S. J. Crawley55, R. A. Creager134,G. Cree33, S. Crépé-Renaudin56, F. Crescioli133, W.A.Cribbs43a,43b, M. Cristinziani24, 
123 


V. Croft117, G. Crosetti40b,40a, A. Cueto96, T. Cuhadar Donszelmann146, A. R. Cukierman150, J. Cummings181, 
M. Curatolo49,J. Cúth97, S. Czekierda82, P. Czodrowski35, M. J. Da Cunha Sargedas De Sousa137a,137b,C. Da Via98, 
W. Dabrowski81a, T. Dado28a,z,T. Dai103,O. Dale17, F. Dallaire107, C. Dallapiccola100,M. Dam39,G. D’amen23b,23a, 
J. R. Dandoy134, M. F. Daneri30, N. P. Dang179,m, A.C.Daniells21, N. D. Dann98, M. Danninger173, M. Dano Hoffmann142, 
V. Dao152, G. Darbo53b,S. Darmora8, J. Dassoulas3, A. Dattagupta128, T. Daubney44,S. D’Auria55, W. Davey24, 
C. David44, T. Davidek140, D.R.Davis47,P. Davison92,E. Dawe102,I. Dawson146,K. De8, R. De Asmundis67a, 
A. De Benedetti125,S.DeCastro23b,23a, S. De Cecco133,N.DeGroot117, P. de Jong118,H.De laTorre104, F. De Lorenzi76, 
A. De Maria51,v, D. De Pedis70a, A. De Salvo70a, U. De Sanctis71a,71b, A. De Santo153, K. De Vasconcelos Corga99, 
J. B. De Vivie De Regie129, R. Debbe29, C. Debenedetti143, D. V. Dedovich77, N. Dehghanian3, I. Deigaard118, 
M. Del Gaudio40b,40a, J. Del Peso96, D. Delgove129, F. Deliot142, C. M. Delitzsch7, M. Della Pietra67a,67b, D. Della Volpe52, 
A. Dell’Acqua35, L. Dell’Asta25, M. Dell’Orso69a,69b, M. Delmastro5, C. Delporte129, P. A. Delsart56, D. A. DeMarco165, 
S. Demers181, M. Demichev77, A. Demilly133, S. P. Denisov121, D. Denysiuk142, L. D’Eramo133, D. Derendarz82, 
J. E. Derkaoui34d, F. Derue133,P. Dervan88, K. Desch24, C. Deterre44, K. Dette165, M.R.Devesa30,P. O. Deviveiros35, 
A. Dewhurst141, S. Dhaliwal26, F.A.Di Bello52, A. Di Ciaccio71a,71b, L. Di Ciaccio5, W. K. Di Clemente134, 
C. Di Donato67a,67b, A. Di Girolamo35, B. Di Girolamo35, B. Di Micco72a,72b, R. Di Nardo35, K. F. Di Petrillo57, 
A. Di Simone50, R. Di Sipio165, D. Di Valentino33, C. Diaconu99, M. Diamond165,F. A. Dias39, M.A.Diaz144a, 
E. B. Diehl103, J. Dietrich19, S. Díez Cornell44, A. Dimitrievska16, J. Dingfelder24, P. Dita27b, S. Dita27b, F. Dittus35, 
F. Djama99, T. Djobava157b, J. I. Djuvsland59a, M.A.B.Do Vale78c, D. Dobos35, M. Dobre27b, C. Doglioni94, 
J. Dolejsi140, Z. Dolezal140, M. Donadelli78d, S. Donati69a,69b, P. Dondero68a,68b, J. Donini37, M. D’Onofrio88, 
J. Dopke141,A. Doria67a, M.T.Dova86, A.T.Doyle55, E. Drechsler51,M. Dris10,Y. Du58b, J. Duarte-Campderros159, 
A. Dubreuil52, E. Duchovni178, G. Duckeck112, A. Ducourthial133, O.A.Ducu107,y, D. Duda118, A. Dudarev35, 
A. C. Dudder97, E. M. Dufﬁeld18, L. Duﬂot129, M. Dührssen35,C. Dülsen180, M. Dumancic178, A. E. Dumitriu27b,e, 
A. K. Duncan55, M. Dunford59a, H. Duran Yildiz4a, M. Düren54, A. Durglishvili157b, D. Duschinger46, B. Dutta44, 
D. Duvnjak1, M. Dyndal44, B. S. Dziedzic82, C. Eckardt44, K. M. Ecker113, R. C. Edgar103,T. Eifert35, G. Eigen17, 
K. Einsweiler18,T. Ekelof170, M. El Kacimi34c, R. El Kosseiﬁ99, V. Ellajosyula99, M. Ellert170, S. Elles5, F. Ellinghaus180, 
A. A. Elliot174, N. Ellis35, J. Elmsheuser29, M. Elsing35, D. Emeliyanov141, Y. Enari161, O. C. Endner97, J. S. Ennis176, 
J. Erdmann45, A. Ereditato20,M. Ernst29, S. Errede171, M. Escalier129, C. Escobar172, B. Esposito49, O. Estrada Pastor172, 
A. I. Etienvre142, E. Etzion159, H. Evans63, A. Ezhilov135, M. Ezzi34e, F. Fabbri23b,23a, L. Fabbri23b,23a, V. Fabiani117, 
G. Facini92, R. M. Fakhrutdinov121, S. Falciano70a, R. J. Falla92, J. Faltova35, Y. Fang15a, M. Fanti66a,66b, A. Farbin8, 
A. Farilla72a,C. Farina136, E.M.Farina68a,68b, T. Farooque104, S. Farrell18, S. M. Farrington176, P. Farthouat35, 
F. Fassi34e, P. Fassnacht35, D. Fassouliotis9, M. Faucci Giannelli48, A. Favareto53b,53a, W. J. Fawcett132, L. Fayard129, 
O. L. Fedin135,r, W. Fedorko173, S. Feigl131, L. Feligioni99, C. Feng58b, E. J. Feng35, H. Feng103, M. J. Fenton55, 
A. B. Fenyuk121, L. Feremenga8, P. Fernandez Martinez172, S. Fernandez Perez14, J. Ferrando44, A. Ferrari170, 
P. Ferrari118, R. Ferrari68a, D. E. Ferreira de Lima59b, A. Ferrer172, D. Ferrere52, C. Ferretti103, F. Fiedler97, M. Filipuzzi44, 
A. Filipˇciˇc89, F. Filthaut117, M. Fincke-Keeler174, K. D. Finelli154, M.C.N.Fiolhais137a,137c,b,L. Fiorini172, A. Fischer2, 
C. Fischer14, J. Fischer180, W. C. Fisher104, N. Flaschel44, I. Fleck148, P. Fleischmann103, R. R. M. Fletcher134, T. Flick180, 
B. M. Flierl112, L. R. Flores Castillo61a,M. J. Flowerdew113, G. T. Forcolin98, A. Formica142, F. A. Förster14,A. C. Forti98, 
A. G. Foster21, D. Fournier129,H. Fox87, S. Fracchia146, P. Francavilla133, M. Franchini23b,23a, S. Franchino59a, 
D. Francis35, L. Franconi131, M. Franklin57,M. Frate169, M. Fraternali68a,68b, D. Freeborn92, S. M. Fressard-Batraneanu35, 
B. Freund107, D. Froidevaux35, J.A.Frost132, C. Fukunaga162, T. Fusayasu114, J. Fuster172, C. Gabaldon56, 
O. Gabizon158, A. Gabrielli23b,23a, A. Gabrielli18, G. P. Gach81a, S. Gadatsch35, S. Gadomski52, G. Gagliardi53b,53a, 
L. G. Gagnon107, C. Galea117, B. Galhardo137a,137c, E.J.Gallas132, B.J.Gallop141, P. Gallus139, G. Galster39, 
K. K. Gan123, S. Ganguly37,Y. Gao88,Y. S. Gao150,n, C. García172, J. E. García Navarro172, J. A. García Pascual15a, 
M. Garcia-Sciveres18, R. W. Gardner36, N. Garelli150, V. Garonne131, A. Gascon Bravo44, K. Gasnikova44, C. Gatti49, 
A. Gaudiello53b,53a, G. Gaudio68a, I.L.Gavrilenko108,C. Gay173, G. Gaycken24, E.N.Gazis10, C.N.P.Gee141, 
J. Geisen51, M. Geisen97, M. P. Geisler59a, K. Gellerstedt43a,43b, C. Gemme53b, M. H. Genest56, C. Geng103, 
S. Gentile70a,70b, C. Gentsos160, S. George91, D. Gerbaudo14, A. Gershon159, G. Gessner45, S. Ghasemi148, M. Ghneimat24, 
B. Giacobbe23b, S. Giagu70a,70b, N. Giangiacomi23b,23a, P. Giannetti69a,S. M. Gibson91, M. Gignac173, M. Gilchriese18, 
D. Gillberg33, G. Gilles180, D. M. Gingrich3,ax, M. P. Giordani64a,64c,F. M. Giorgi23b, P. F. Giraud142, P. Giromini57, 
G. Giugliarelli64a,64c, D. Giugni66a, F. Giuli132, C. Giuliani113, M. Giulini59b, B. K. Gjelsten131, S. Gkaitatzis160, 
I. Gkialas9,l, E. L. Gkougkousis14, P. Gkountoumis10, L. K. Gladilin111,C. Glasman96, J. Glatzer14, P. C. F. Glaysher44, 
A. Glazov44, M. Goblirsch-Kolb26, J. Godlewski82, S. Goldfarb102, T. Golling52, D. Golubkov121, A. Gomes137a,137b, 
R. Goncalves Gama78b, J. Goncalves Pinto Firmino Da Costa142, R. Gonçalo137a, G. Gonella50, L. Gonella21, 
123 


A. Gongadze77, S. González de la Hoz172, S. Gonzalez-Sevilla52, L. Goossens35, P. A. Gorbounov109, H. A. Gordon29, 
I. Gorelov116,B. Gorini35,E. Gorini65a,65b,A. Gorišek89,A. T. Goshaw47,C. Gössling45,M.I.Gostkin77,C. A. Gottardo24, 
C. R. Goudet129, D. Goujdami34c, A. G. Goussiou145, N. Govender32b,c, E. Gozani158, L. Graber51, I. Grabowska-Bold81a, 
P. O. J. Gradin170, J. Gramling169,E. Gramstad131, S. Grancagnolo19, V. Gratchev135, P.M.Gravila27f,C. Gray55, 
H. M. Gray18, Z. D. Greenwood93,al, C. Grefe24, K. Gregersen92, I.M.Gregor44, P. Grenier150,K. Grevtsov5,J. Grifﬁths8, 
A. A. Grillo143,K. Grimm87, S. Grinstein14,aa, Ph. Gris37,J.-F. Grivaz129,S. Groh97, E. Gross178, J. Grosse-Knetter51, 
G. C. Grossi93, Z. J. Grout92, A. Grummer116, L. Guan103, W. Guan179, J. Guenther74, F. Guescini166a, D. Guest169, 
O. Gueta159,B. Gui123, E. Guido53a,53b, T. Guillemin5, S. Guindon35,U. Gul55, C. Gumpert35,J. Guo58c,W. Guo103, 
Y. Guo58a,u, R. Gupta41, S. Gupta132, G. Gustavino125, B. J. Gutelman158, P. Gutierrez125, N. G. Gutierrez Ortiz92, 
C. Gutschow92, C. Guyot142, M. P. Guzik81a, C. Gwenlan132, C. B. Gwilliam88, A. Haas122, C. Haber18, H. K. Hadavand8, 
N. Haddad34e, A. Hadef99, S. Hageböck24, M. Hagihara167, H. Hakobyan182,*, M. Haleem44,J.Haley126, G. Halladjian104, 
G. D. Hallewell99, K. Hamacher180, P. Hamal127, K. Hamano174, A. Hamilton32a, G.N.Hamity146, P. G. Hamnett44, 
L. Han58a,S. Han15d, K. Hanagaki79,x, K. Hanawa161, M. Hance143, B. Haney134, P. Hanke59a, J. B. Hansen39, 
J. D. Hansen39, M. C. Hansen24, P. H. Hansen39,K. Hara167,A. S. Hard179, T. Harenberg180,F. Hariri129, S. Harkusha105, 
R. D. Harrington48, P.F.Harrison176, N. M. Hartmann112, Y. Hasegawa147,A. Hasib48, S. Hassani142, S. Haug20, 
R. Hauser104, L. Hauswald46, L. B. Havener38, M. Havranek139, C.M.Hawkes21, R. J. Hawkings35, D. Hayakawa163, 
D. Hayden104, C. P. Hays132, J.M.Hays90, H. S. Hayward88, S. J. Haywood141, S.J.Head21, T. Heck97, V. Hedberg94, 
L. Heelan8, S. Heer24, K. K. Heidegger50,S. Heim44,T. Heim18, B. Heinemann44,as, J. J. Heinrich112, L. Heinrich122, 
C. Heinz54, J. Hejbal138, L. Helary35,A. Held173, S. Hellman43a,43b, C. Helsens35, R. C. W. Henderson87, Y. Heng179, 
S. Henkelmann173, A. M. Henriques Correia35, S. Henrot-Versille129, G. H. Herbert19, H. Herde26, V. Herget175, 
Y. Hernández Jiménez32c,H. Herr97,G. Herten50, R. Hertenberger112,L. Hervas35,T. C. Herwig134,G. G. Hesketh92, 
N. P. Hessey166a, J. W. Hetherly41, S. Higashino79, E. Higón-Rodriguez172, K. Hildebrand36, E. Hill174, J. C. Hill31, 
K. H. Hiller44, S. J. Hillier21, M. Hils46, I. Hinchliffe18,M. Hirose50, D. Hirschbuehl180, B. Hiti89, O. Hladik138, 
X. Hoad48, J. Hobbs152,N. Hod166a, M. C. Hodgkinson146, P. Hodgson146, A. Hoecker35, M. R. Hoeferkamp116, 
F. Hoenig112, D. Hohn24,T. R. Holmes36, M. Homann45, S. Honda167, T. Honda79, T. M. Hong136, B. H. Hooberman171, 
W. H. Hopkins128,Y. Horii115,A. J. Horton149, J-Y. Hostachy56,S. Hou155, A. Hoummada34a,J. Howarth98,J. Hoya86, 
M. Hrabovsky127, J. Hrdinka35, I. Hristova19, J. Hrivnac129, A. Hrynevich106, T. Hryn’ova5,P. J. Hsu62,S.-C. Hsu145, 
Q. Hu58a,S. Hu58c, Y. Huang15a, Z. Hubacek139, F. Hubaut99, F. Huegging24, T. B. Huffman132, E. W. Hughes38, 
G. Hughes87, M. Huhtinen35,P. Huo152, N. Huseynov77,ah,J. Huston104,J. Huth57, G. Iacobucci52, G. Iakovidis29, 
I. Ibragimov148, L. Iconomidou-Fayard129, Z. Idrissi34e, P. Iengo35, O. Igonkina118,ad,T. Iizawa177,Y. Ikegami79, 
M. Ikeno79, Y. Ilchenko11, D. Iliadis160, N. Ilic150, G. Introzzi68a,68b, P. Ioannou9,*, M. Iodice72a, K. Iordanidou38, 
V. Ippolito57, M. F. Isacson170, N. Ishijima130,M. Ishino161, M. Ishitsuka163, C. Issever132, S. Istin12c,F. Ito167, 
J. M. Iturbe Ponce61a, R. Iuppa73a,73b, H. Iwasaki79, J.M.Izen42, V. Izzo67a, S. Jabbar3, P. Jackson1, R. M. Jacobs24, 
V. Jain2, K. B. Jakobi97, K. Jakobs50, S. Jakobsen74, T. Jakoubek138,D. O. Jamin126, D. K. Jana93, R. Jansky52, J. Janssen24, 
M. Janus51, P. A. Janus81a,G. Jarlskog94, N. Javadov77,ah,T. Jav˚urek50, M. Javurkova50, F. Jeanneau142, L. Jeanty18, 
J. Jejelava157a,ai, A. Jelinskas176, P. Jenni50,d,C. Jeske176, S. Jézéquel5,H. Ji179,J. Jia152, H. Jiang76, Y. Jiang58a, 
Z. Jiang150,s, S. Jiggins92, J. Jimenez Pena172,S. Jin15a, A. Jinaru27b, O. Jinnouchi163,H. Jivan32c, P. Johansson146, 
K. A. Johns7, C. A. Johnson63, W. J. Johnson145, K. Jon-And43a,43b, R. W. L. Jones87, S. D. Jones153, S. Jones7, 
T. J. Jones88, J. Jongmanns59a, P. M. Jorge137a,137b, J. Jovicevic166a,X. Ju179, A. Juste Rozas14,aa, A. Kaczmarska82, 
M. Kado129, H. Kagan123, M. Kagan150, S.J.Kahn99,T. Kaji177, E. Kajomovitz47, C. W. Kalderon94, A. Kaluza97, 
S. Kama41, A. Kamenshchikov121, N. Kanaya161, L. Kanjir89, V. A. Kantserov110, J. Kanzaki79, B. Kaplan122, 
L. S. Kaplan179,D. Kar32c, K. Karakostas10, N. Karastathis10, M. J. Kareem51, E. Karentzos10, S. N. Karpov77, 
Z. M. Karpova77, K. Karthik122, V. Kartvelishvili87, A. N. Karyukhin121, K. Kasahara167, L. Kashif179, R.D.Kass123, 
A. Kastanas151, Y. Kataoka161,C. Kato161,A. Katre52, J. Katzy44, K. Kawade80, K. Kawagoe85, T. Kawamoto161, 
G. Kawamura51, E.F.Kay88, V. F. Kazanin120b,120a, R. Keeler174, R. Kehoe41, J. S. Keller33, E. Kellermann94, 
J. J. Kempster91, J Kendrick21, H. Keoshkerian165, O. Kepka138, S. Kersten180, B. P. Kerševan89, R.A.Keyes101, 
M. Khader171, F. Khalil-Zada13, A. Khanov126, A.G.Kharlamov120b,120a, T. Kharlamova120b,120a, A. Khodinov164, 
T. J. Khoo52, V. Khovanskiy109,*, E. Khramov77, J. Khubua157b,S. Kido80, C. R. Kilby91,H. Y. Kim8,S. H. Kim167, 
Y. K. Kim36,N. Kimura160,O. M. Kind19,B. T. King88, D. Kirchmeier46,J. Kirk141, A. E. Kiryunin113, T. Kishimoto161, 
D. Kisielewska81a, V. Kitali44,O. Kivernyk5, E. Kladiva28b,*, T. Klapdor-Kleingrothaus50, M. H. Klein103, M. Klein88, 
U. Klein88, K. Kleinknecht97, P. Klimek119, A. Klimentov29, R. Klingenberg45,*, T. Klingl24, T. Klioutchnikova35, 
P. Kluit118, S. Kluth113, E. Kneringer74, E. B. F. G. Knoops99, A. Knue113, A. Kobayashi161, D. Kobayashi163, 
T. Kobayashi161, M. Kobel46, M. Kocian150, P. Kodys140,T. Koffas33,E. Koffeman118, M. K. Köhler178, N. M. Köhler113, 
123 


T. Koi150,M. Kolb59b, I. Koletsou5, A. A. Komar108,*, T. Kondo79, N. Kondrashova58c, K. Köneke50, A. C. König117, 
T. Kono79,ar, R. Konoplich122,an, N. Konstantinidis92, R. Kopeliansky63, S. Koperny81a, A. K. Kopp50,K. Korcyl82, 
K. Kordas160,A. Korn92, A. A. Korol120a,120b,aq, I. Korolkov14, E. V. Korolkova146, O. Kortner113, S. Kortner113, 
T. Kosek140, V. V. Kostyukhin24,A. Kotwal47, A. Koulouris10, A. Kourkoumeli-Charalampidi68a,68b, C. Kourkoumelis9, 
E. Kourlitis146, V. Kouskoura29, A. B. Kowalewska82, R. Kowalewski174, T. Z. Kowalski81a, C. Kozakai161, 
W. Kozanecki142, A. S. Kozhin121, V.A.Kramarenko111, G. Kramberger89, D. Krasnopevtsev110, M.W.Krasny133, 
A. Krasznahorkay35, D. Krauss113, J.A.Kremer81a, J. Kretzschmar88, K. Kreutzfeldt54, P. Krieger165, K. Krizka18, 
K. Kroeninger45, H. Kroha113,J. Kroll138,J. Kroll134, J. Kroseberg24, J. Krstic16, U. Kruchonak77, H. Krüger24, 
N. Krumnack76,M. C. Kruse47, T. Kubota102, H. Kucuk92, S. Kuday4b, J. T. Kuechler180, S. Kuehn35, A. Kugel59a, 
F. Kuger175, T. Kuhl44, V. Kukhtin77, R. Kukla99, Y. Kulchitsky105, S. Kuleshov144b, Y. P. Kulinich171, M. Kuna70a,70b, 
T. Kunigo83, A. Kupco138, T. Kupfer45, O. Kuprash159, H. Kurashige80, L. L. Kurchaninov166a, Y. A. Kurochkin105, 
M. G. Kurth15d,V. Kus138, E. S. Kuwertz174, M. Kuze163, J. Kvita127,T. Kwan174, D. Kyriazopoulos146,A. La Rosa113, 
J. L. La Rosa Navarro78d, L. La Rotonda40b,40a, F. La Ruffa40b,40a, C. Lacasta172, F. Lacava70a,70b, J. Lacey44, 
D. P. J. Lack98, H. Lacker19, D. Lacour133, E. Ladygin77, R. Lafaye5, B. Laforge133,S. Lai51, S. Lammers63, W. Lampl7, 
E. Lançon29, U. Landgraf50, M. P. J. Landon90, M. C. Lanfermann52, V. S. Lang44, J. C. Lange14, R. J. Langenberg35, 
A. J. Lankford169, F. Lanni29, K. Lantzsch24, A. Lanza68a, A. Lapertosa53b,53a, S. Laplace133, J. F. Laporte142,T. Lari66a, 
F. Lasagni Manghi23b,23a, M. Lassnig35,T. S. Lau61a, P. Laurelli49, W. Lavrijsen18,A. T. Law143, P. Laycock88, 
T. Lazovich57, M. Lazzaroni66a,66b,B. Le102,O. Le Dortz133,E. Le Guirriec99, E. P. Le Quilleuc142, M. LeBlanc174, 
T. LeCompte6, F. Ledroit-Guillon56,C. A. Lee29, G.R.Lee141,i,L. Lee57,S. C. Lee155, B. Lefebvre101, G. Lefebvre133, 
M. Lefebvre174, F. Legger112, C. Leggett18, G. Lehmann Miotto35,X. Lei7, W. A. Leight44, M.A.L.Leite78d, R. Leitner140, 
D. Lellouch178, B. Lemmer51, K.J.C.Leney92, T. Lenz24, B. Lenzi35, R. Leone7, S. Leone69a, C. Leonidopoulos48, 
G. Lerner153,C. Leroy107, A.A.J.Lesage142, C.G.Lester31, M. Levchenko135, J. Levêque5,D. Levin103, L.J.Levinson178, 
M. Levy21,D. Lewis90,B. Li58a,u,C-Q. Li58a,am,H. Li152,L. Li58c,Q. Li15d,Q. Y. Li58a,S. Li47,X. Li58c,Y. Li148, 
Z. Liang15a, B. Liberti71a, A. Liblong165,K. Lie61c, J. Liebal24, W. Liebig17, A. Limosani154,S. C. Lin156, T.H.Lin97, 
R. A. Linck63, B. E. Lindquist152, A.L.Lionti52, E. Lipeles134, A. Lipniacka17, M. Lisovyi59b, T.M.Liss171,au, 
A. Lister173, A.M.Litke143,B. Liu76,H. B. Liu29,H. Liu103, J.B.Liu58a, J.K.K.Liu132,J. Liu58b,K. Liu99,L. Liu171, 
M. Liu58a,Y. L. Liu58a,Y. W. Liu58a,M. Livan68a,68b,A. Lleres56, J. Llorente Merino15a,S. L. Lloyd90,C. Y. Lo61b, 
F. Lo Sterzo155, E. M. Lobodzinska44, P. Loch7, F. K. Loebinger98,K. M. Loew26, A. Loginov181,*, T. Lohse19, 
K. Lohwasser146, M. Lokajicek138, B. A. Long25, J. D. Long171, R.E.Long87, L. Longo65a,65b, K. A. Looper123, 
J. A. Lopez144b, D. Lopez Mateos57, I. Lopez Paz14, A. Lopez Solis133, J. Lorenz112, N. Lorenzo Martinez5, 
M. Losada22, P. J. Lösel112,A. Lösle50,X. Lou15a, A. Lounis129, J. Love6, P. A. Love87,H. Lu61a,N. Lu103,Y. J. Lu62, 
H. J. Lubatti145, C. Luci70a,70b, A. Lucotte56, C. Luedtke50, F. Luehring63, W. Lukas74, L. Luminari70a, O. Lundberg43a,43b, 
B. Lund-Jensen151,M. S. Lutz100, P. M. Luzi133, D. Lynn29, R. Lysak138,E. Lytken94,F. Lyu15a, V. Lyubushkin77, 
H. Ma29, L.L.Ma58b,Y. Ma58b, G. Maccarrone49, A. Macchiolo113, C. M. Macdonald146, J. Machado Miguens134,137b, 
D. Madaffari172, R. Madar37, W. F. Mader46, A. Madsen44, J. Maeda80, S. Maeland17, T. Maeno29, A. S. Maevskiy111, 
V. Magerl50, J. Mahlstedt118, C. Maiani129, C. Maidantchik78b, A.A.Maier113, T. Maier112,A. Maio137a,137b,137d, 
O. Majersky28a,S. Majewski128, Y. Makida79, N. Makovec129, B. Malaescu133, Pa. Malecki82, V. P. Maleev135, 
F. Malek56, U. Mallik75, D. Malon6, C. Malone31, S. Maltezos10, S. Malyukov35, J. Mamuzic172, G. Mancini49, 
I. Mandi´c89, J. Maneira137a,137b, L. Manhaes de Andrade Filho78a, J. Manjarres Ramos46, K. H. Mankinen94, A. Mann112, 
A. Manousos35, B. Mansoulie142, J. D. Mansour15a, R. Mantifel101, M. Mantoani51, S. Manzoni66a,66b, L. Mapelli35, 
G. Marceca30, L. March52, L. Marchese132, G. Marchiori133, M. Marcisovsky138, M. Marjanovic37,D. E. Marley103, 
F. Marroquim78b, S. P. Marsden98, Z. Marshall18, M.U.FMartensson170, S. Marti-Garcia172, C. B. Martin123, 
T. A. Martin176, V.J.Martin48, B. Martin dit Latour17, M. Martinez14,aa, V. I. Martinez Outschoorn171, S. Martin-Haugh141, 
V. S. Martoiu27b, A. C. Martyniuk92, A. Marzin35, L. Masetti97, T. Mashimo161, R. Mashinistov108,J. Masik98, 
A. L. Maslennikov120b,120a, L. Massa71a,71b, P. Mastrandrea5, A. Mastroberardino40b,40a, T. Masubuchi161, P. Mättig180, 
J. Maurer27b,B. Maˇcek89, S. J. Maxﬁeld88, D. A. Maximov120b,120a, R. Mazini155, I. Maznas160, S. M. Mazza66a,66b, 
N. C. Mc Fadden116, G. Mc Goldrick165,S. P. Mc Kee103, A. McCarn103, R. L. McCarthy152, T. G. McCarthy113, 
L. I. McClymont92, E. F. McDonald102, J. A. Mcfayden35, G. Mchedlidze51, S. J. McMahon141, P. C. McNamara102, 
C. J. McNicol176, R. A. McPherson174,af, S. Meehan145,T. M. Megy50, S. Mehlhase112, A. Mehta88, T. Meideck56, 
B. Meirose42, D. Melini172,h, B. R. Mellado Garcia32c, J. D. Mellenthin51,M. Melo28a, F. Meloni20, A. Melzer24, 
S. B. Menary98, L. Meng88, X. T. Meng103, A. Mengarelli23b,23a, S. Menke113, E. Meoni40b,40a, S. Mergelmeyer19, 
C. Merlassino20,P. Mermod52, L. Merola67a,67b, C. Meroni66a, F.S.Merritt36, A. Messina70a,70b, J. Metcalfe6, 
A. S. Mete169, C. Meyer134, J. Meyer118, J-P. Meyer142, H. Meyer Zu Theenhausen59a, F. Miano153, R. P. Middleton141, 
123 


48

S. Miglioranzi53a,53b, L. Mijovi´c, G. Mikenberg178, M. Mikestikova138, M. Mikuž89, M. Milesi102, A. Milic165, 
D. A. Millar90, D. W. Miller36, C. Mills48, A. Milov178, D. A. Milstead43a,43b, A. A. Minaenko121, Y. Minami161, 
I. A. Minashvili157b, A. I. Mincer122, B. Mindur81a, M. Mineev77, Y. Minegishi161, Y. Ming179, L. M. Mir14, 
K. P. Mistry134, T. Mitani177, J. Mitrevski112, V. A. Mitsou172, A. Miucci20, P. S. Miyagawa146, A. Mizukami79, 
J. U. Mjörnmark94, T. Mkrtchyan182, M. Mlynarikova140,T. Moa43a,43b, K. Mochizuki107, P. Mogg50, S. Mohapatra38, 
S. Molander43a,43b, R. Moles-Valls24, M. C. Mondragon104, K. Mönig44, J. Monk39, E. Monnier99, A. Montalbano152, 
J. Montejo Berlingen35, F. Monticelli86, S. Monzani66a, R. W. Moore3, N. Morange129, D. Moreno22, M. Moreno Llácer35, 
P. Morettini53b, S. Morgenstern35,D. Mori149,T. Mori161,M. Morii57, M. Morinaga177, V. Morisbak131,A. K. Morley35, 
G. Mornacchi35, J.D.Morris90,L. Morvaj152, P. Moschovakos10, M. Mosidze157b,H. J. Moss146,J. Moss150,o, 
K. Motohashi163, R. Mount150, E. Mountricha29,E. J. W. Moyse100, S. Muanza99, F. Mueller113, J. Mueller136, 
R. S. P. Mueller112, D. Muenstermann87, P. Mullen55, G. A. Mullier20, F. J. Munoz Sanchez98, W.J.Murray176,141, 
H. Musheghyan35, M. Muškinja89, A. G. Myagkov121,ao, M. Myska139, B. P. Nachman18, O. Nackenhorst52, K. Nagai132, 
R. Nagai79,ar, K. Nagano79, Y. Nagasaka60, K. Nagata167, M. Nagel50, E. Nagy99, A.M.Nairz35, Y. Nakahama115, 
K. Nakamura79, T. Nakamura161, I. Nakano124, R. F. Naranjo Garcia44, R. Narayan11, D.I.Narrias Villar59a, 
I. Naryshkin135, T. Naumann44, G. Navarro22, R. Nayyar7, H. A. Neal103,*, P. Y. Nechaeva108, T.J.Neep142, 
A. Negri68a,68b,M. Negrini23b, S. Nektarijevic117, C. Nellist129,A. Nelson169,M. E. Nelson132, S. Nemecek138, 
P. Nemethy122, M. Nessi35,f, M. S. Neubauer171, M. Neumann180, P. R. Newman21,T. Y. Ng61c, T. Nguyen Manh107, 
R. B. Nickerson132, R. Nicolaidou142, J. Nielsen143, V. Nikolaenko121,ao, I. Nikolic-Audit133, K. Nikolopoulos21, 
J. K. Nilsen131, P. Nilsson29, Y. Ninomiya161,A. Nisati70a,N. Nishu58c, R. Nisius113, I. Nitsche45, T. Nitta177, T. Nobe161, 
Y. Noguchi83, M. Nomachi130, I. Nomidis33, M.A.Nomura29, T. Nooney90, M. Nordberg35, N. Norjoharuddeen132, 
O. Novgorodova46, M. Nozaki79, L. Nozka127, K. Ntekas169,E. Nurse92,F. Nuti102, F. G. Oakham33,ax, H. Oberlack113, 
T. Obermann24, J. Ocariz133, A. Ochi80, I. Ochoa38, J. P. Ochoa-Ricoux144a, K. O’Connor26,S. Oda85, S. Odaka79, 
A. Oh98,S. H. Oh47,C. C. Ohm18, H. Ohman170,H. Oide53b,53a,H. Okawa167, Y. Okumura161, T. Okuyama79, 
A. Olariu27b, L. F. Oleiro Seabra137a, S. A. Olivares Pino144a, D. Oliveira Damazio29, A. Olszewski82, J. Olszowska82, 
D. C. O’Neil149, A. Onofre137a,137e, K. Onogi115, P.U.E.Onyisi11, H. Oppen131, M.J.Oreglia36,Y. Oren159, 
D. Orestano72a,72b, N. Orlando61b, A.A.O’Rourke44,R. S. Orr165, B. Osculati53b,53a,*, V. O’Shea55, R. Ospanov58a, 
G. Otero y Garzon30, H. Otono85, M. Ouchrif34d, F. Ould-Saada131, A. Ouraou142, K. P. Oussoren118, Q. Ouyang15a, 
M. Owen55, R. E. Owen21, V. E. Ozcan12c, N. Ozturk8, K. Pachal149, A. Pacheco Pages14, L. Pacheco Rodriguez142, 
C. Padilla Aranda14, S. Pagan Griso18, M. Paganini181, F. Paige29,*, G. Palacino63, S. Palazzo40b,40a, S. Palestini35, 
M. Palka81b, D. Pallin37, E. St. Panagiotopoulou10, I. Panagoulias10, C. E. Pandini69a,69b, J. G. Panduro Vazquez91, 
P. Pani35, S. Panitkin29, D. Pantea27b, L. Paolozzi52, T. D. Papadopoulou10, K. Papageorgiou9,l, A. Paramonov6, 
D. Paredes Hernandez181,A. J. Parker87,K. A. Parker44, M.A.Parker31, F. Parodi53b,53a, J. A. Parsons38, U. Parzefall50, 
V. R. Pascuzzi165, J. M. P. Pasner143, E. Pasqualucci70a, S. Passaggio53b,F. Pastore91, S. Pataraia97, J. R. Pater98, 
T. Pauly35, B. Pearson113, S. Pedraza Lopez172, R. Pedro137a,137b, S. V. Peleganchuk120b,120a, O. Penc138, C. Peng15d, 
H. Peng58a, J. Penwell63, B. S. Peralva78a, M.M.Perego142, D. V. Perepelitsa29, F. Peri19, L. Perini66a,66b, H. Pernegger35, 
S. Perrella67a,67b, R. Peschke44, V. D. Peshekhonov77,*, K. Peters44, R. F. Y. Peters98, B. A. Petersen35, T. C. Petersen39, 
E. Petit56, A. Petridis1, C. Petridou160, P. Petroff129, E. Petrolo70a, M. Petrov132, F. Petrucci72a,72b, N. E. Pettersson100, 
A. Peyaud142, R. Pezoa144b, F. H. Phillips104, P. W. Phillips141, G. Piacquadio152, E. Pianori176, A. Picazio100, 
E. Piccaro90,M. A. Pickering132, R. Piegaia30, J. E. Pilcher36, A. D. Pilkington98, A.W.J.Pin98, M. Pinamonti71a,71b, 
J. L. Pinfold3,H. Pirumov44, M. Pitt178, L. Plazak28a, M.-A. Pleier29,V. Pleskot97, E. Plotnikova77,D. Pluth76, 
P. Podberezko120b,120a, R. Poettgen94, R. Poggi68a,68b, L. Poggioli129, I. Pogrebnyak104, D. Pohl24, G. Polesello68a, 
A. Poley44, A. Policicchio40b,40a, R. Polifka35, A. Polini23b, C.S.Pollard55, V. Polychronakos29, K. Pommès35, 
D. Ponomarenko110, L. Pontecorvo70a, G. A. Popeneciu27d, S. Pospisil139, K. Potamianos18, I.N.Potrap77, C. J. Potter31, 
H. Potti11, T. Poulsen94, J. Poveda35, M. E. Pozo Astigarraga35, P. Pralavorio99, A. Pranko18,S. Prell76,D. Price98, 
M. Primavera65a, S. Prince101, N. Proklova110, K. Prokoﬁev61c,F. Prokoshin144b, S. Protopopescu29, J. Proudfoot6, 
M. Przybycien81a, A. Puri171, P. Puzo129,J. Qian103,G. Qin55,Y. Qin98, A. Quadt51, M. Queitsch-Maitland44, 
D. Quilty55, S. Raddum131, V. Radeka29, V. Radescu132, S. K. Radhakrishnan152, P. Radloff128, P. Rados102, 
F. Ragusa66a,66b, G. Rahal95, J.A.Raine98, S. Rajagopalan29, C. Rangel-Smith170, T. Rashid129, S. Raspopov5, 
M. G. Ratti66a,66b, D. M. Rauch44, F. Rauscher112,S. Rave97, I. Ravinovich178, J.H.Rawling98, M. Raymond35, 
A. L. Read131, N. P. Readioff56, M. Reale65a,65b, D. M. Rebuzzi68a,68b, A. Redelbach175, G. Redlinger29, R. Reece143, 
R. G. Reed32c,K. Reeves42, L. Rehnisch19, J. Reichert134,A. Reiss97, C. Rembser35,H. Ren15d, M. Rescigno70a, 
S. Resconi66a, E. D. Resseguie134, S. Rettie173, E. Reynolds21, O. L. Rezanova120b,120a, P. Reznicek140, R. Rezvani107, 
R. Richter113, S. Richter92, E. Richter-Was81b,O. Ricken24, M. Ridel133, P. Rieck113, C.J.Riegel180, J. Rieger51, 
123 


O. Rifki125, M. Rijssenbeek152, A. Rimoldi68a,68b, M. Rimoldi20, L. Rinaldi23b, G. Ripellino151, B. Risti´c35,E. Ritsch35, 
I. Riu14, F. Rizatdinova126, E. Rizvi90, C. Rizzi14, R. T. Roberts98, S. H. Robertson101,af, A. Robichaud-Veronneau101, 
D. Robinson31, J. E. M. Robinson44, A. Robson55, E. Rocco97, C. Roda69a,69b, Y. Rodina99,ab, S. Rodriguez Bosca172, 
A. Rodriguez Perez14, D. Rodriguez Rodriguez172,S. Roe35, C.S.Rogan57, O. Røhne131, J. Roloff57, A. Romaniouk110, 
M. Romano23b,23a, S. M. Romano Saez37, E. Romero Adam172, N. Rompotis88, M. Ronzani50, L. Roos133, S. Rosati70a, 
K. Rosbach50,P. Rose143,N-A. Rosien51, E. Rossi67a,67b, L. P. Rossi53b, J.H.N.Rosten31,R. Rosten145, M. Rotaru27b, 
J. Rothberg145, D. Rousseau129, A. Rozanov99, Y. Rozen158, X. Ruan32c, F. Rubbo150, F. Rühr50, A. Ruiz-Martinez33, 
Z. Rurikova50, N. A. Rusakovich77, H. L. Russell101, J. P. Rutherfoord7, N. Ruthmann35, Y. F. Ryabov135, 
M. Rybar171, G. Rybkin129,S. Ryu6, A. Ryzhov121, G. F. Rzehorz51, A. F. Saavedra154, G. Sabato118, S. Sacerdoti30, 
H. F-W. Sadrozinski143, R. Sadykov77, F. Safai Tehrani70a, P. Saha119, M. Sahinsoy59a, M. Saimpert44, M. Saito161, 
T. Saito161, H. Sakamoto161, Y. Sakurai177, G. Salamanna72a,72b, J. E. Salazar Loyola144b, D. Salek118, 
P. H. Sales De Bruin170, D. Salihagic113, A. Salnikov150, J. Salt172, D. Salvatore40b,40a, F. Salvatore153, 
A. Salvucci61a,61b,61c, A. Salzburger35, D. Sammel50, D. Sampsonidis160, D. Sampsonidou160, J. Sánchez172, 
V. Sanchez Martinez172, A. Sanchez Pineda64a,64c, H. Sandaker131, R. L. Sandbach90, C. O. Sander44, M. Sandhoff180, 
C. Sandoval22, D. P. C. Sankey141, M. Sannino53b,53a, Y. Sano115, A. Sansoni49, C. Santoni37, H. Santos137a, 
I. Santoyo Castillo153, A. Sapronov77, J. G. Saraiva137a,137d, B. Sarrazin24, O. Sasaki79, K. Sato167, E. Sauvan5, 
G. Savage91,P. Savard165,ax,N. Savic113, C. Sawyer141, L. Sawyer93,al, J. Saxon36, C. Sbarra23b, A. Sbrizzi23b,23a, 
T. Scanlon92, D. A. Scannicchio169, J. Schaarschmidt145, P. Schacht113, B. M. Schachtner112, D. Schaefer35, 
L. Schaefer134, R. Schaefer44, J. Schaeffer97, S. Schaepe24, S. Schaetzel59b, U. Schäfer97, A. C. Schaffer129, 
D. Schaile112, R. D. Schamberger152, V. A. Schegelsky135, D. Scheirich140, M. Schernau169, C. Schiavi53b,53a, S. Schier143, 
L. K. Schildgen24, C. Schillo50, M. Schioppa40b,40a, S. Schlenker35, K. R. Schmidt-Sommerfeld113, K. Schmieden35, 
C. Schmitt97, S. Schmitt44, S. Schmitz97, U. Schnoor50, L. Schoeffel142, A. Schoening59b, B. D. Schoenrock104, 
E. Schopf24, M. Schott97, J. F. P. Schouwenberg117, J. Schovancova35, S. Schramm52, N. Schuh97, A. Schulte97, 
M. J. Schultens24, H-C. Schultz-Coulon59a, H. Schulz19, M. Schumacher50, B. A. Schumm143, Ph. Schune142, 
A. Schwartzman150, T. A. Schwarz103, H. Schweiger98, Ph. Schwemling142, R. Schwienhorst104, A. Sciandra24, 
G. Sciolla26, M. Scornajenghi40b,40a, F. Scuri69a, F. Scutti102, J. Searcy103, P. Seema24, S. C. Seidel116, A. Seiden143, 
J. M. Seixas78b, G. Sekhniaidze67a, K. Sekhon103, S. J. Sekula41, N. Semprini-Cesari23b,23a, S. Senkin37, C. Serfon131, 
L. Serin129, L. Serkin64a,64b, M. Sessa72a,72b, R. Seuster174,H. Severini125,F. Sforza168, A. Sfyrla52, E. Shabalina51, 
N. W. Shaikh43a,43b, L. Y. Shan15a, R. Shang171, J. T. Shank25, M. Shapiro18, P. B. Shatalov109, K. Shaw64a,64b, 
S. M. Shaw98, A. Shcherbakova43a,43b, C. Y. Shehu153, Y. Shen125, N. Sherafati33, P. Sherwood92, L. Shi155,at, 
S. Shimizu80, C. O. Shimmin181, M. Shimojima114, I. P. J. Shipsey132, S. Shirabe85, M. Shiyakova77, J. Shlomi178, 
A. Shmeleva108, D. Shoaleh Saadi107, M. J. Shochet36, S. Shojaii102, D. R. Shope125, S. Shrestha123, E. Shulga110, 
M. A. Shupe7, P. Sicho138, A. M. Sickles171, P. E. Sidebo151, E. Sideras Haddad32c, O. Sidiropoulou175, A. Sidoti23b,23a, 
F. Siegert46, Dj. Sijacki16, J. Silva137a,137d, S. B. Silverstein43a,V. Simak139, L. Simic16, S. Simion129, E. Simioni97, 
B. Simmons92,M.Simon97,P.Sinervo165,N.B.Sinev128, M. Sioli23b,23a, G. Siragusa175,I.Siral103, S. Yu. Sivoklokov111, 
J. Sjölin43a,43b, M. B. Skinner87, P. Skubic125, M. Slater21, T. Slavicek139, M. Slawinska82, K. Sliwa168,R. Slovak140, 
V. Smakhtin178,B. H. Smart5,J. Smiesko28a,N. Smirnov110, S. Yu. Smirnov110,Y. Smirnov110,L. N. Smirnova111, 
O. Smirnova94, J.W.Smith51, M.N.K.Smith38, R. W. Smith38, M. Smizanska87,K. Smolek139, A. A. Snesarev108, 
I. M. Snyder128, S. Snyder29, R. Sobie174,af, F. Socher46, A. Soffer159, A. Søgaard48, D.A.Soh155, G. Sokhrannyi89, 
C. A. Solans Sanchez35, M. Solar139, E. Yu. Soldatov110, U. Soldevila172, A. A. Solodkov121, A. Soloshenko77, 
O. V. Solovyanov121, V. Solovyev135, P. Sommer50, H. Son168, A. Sopczak139, D. Sosa59b, C. L. Sotiropoulou69a,69b, 
R. Soualah64a,64c,k, A. M. Soukharev120b,120a, D. South44, B. C. Sowden91, S. Spagnolo65a,65b, M. Spalla69a,69b, 
M. Spangenberg176, F. Spanò91, D. Sperlich19, F. Spettel113, T. M. Spieker59a, R. Spighi23b, G. Spigo35, L. A. Spiller102, 
M. Spousta140, R.D.St. Denis55,*, A. Stabile66a,66b,R. Stamen59a,S. Stamm19, E. Stanecka82, R. W. Stanek6, 
C. Stanescu72a, M. M. Stanitzki44, B. Stapf118, S. Stapnes131, E. A. Starchenko121, G.H.Stark36, J. Stark56, 
S. H Stark39, P. Staroba138, P. Starovoitov59a,S. Stärz35, R. Staszewski82, P. Steinberg29, B. Stelzer149, H.J.Stelzer35, 
O. Stelzer-Chilton166a, H. Stenzel54, G.A.Stewart55, M. C. Stockton128, M. Stoebe101, G. Stoicea27b, P. Stolte51, 
S. Stonjek113, A. R. Stradling8, A. Straessner46, M. E. Stramaglia20, J. Strandberg151, S. Strandberg43a,43b, M. Strauss125, 
P. Strizenec28b, R. Ströhmer175, D.M.Strom128, R. Stroynowski41, A. Strubig48, S. A. Stucci29, B. Stugu17,N. A. Styles44, 
D. Su150,J. Su136, S. Suchek59a, Y. Sugaya130, M. Suk139, V. V. Sulin108, D.M.S.Sultan73a,73b, S. Sultansoy4c, 
T. Sumida83, S. Sun57, X. Sun3, K. Suruliz153, C.J.E.Suster154, M. R. Sutton153, S. Suzuki79,M. Svatos138, 
M. Swiatlowski36,S. P. Swift2, I. Sykora28a, T. Sykora140,D. Ta50, K. Tackmann44,ac, J. Taenzer159,A. Taffard169, 
R. Taﬁrout166a, E. Tahirovic90, N. Taiblum159, H. Takai29, R. Takashima84, E. H. Takasugi113, T. Takeshita147, 
123 


Y. Takubo79, M. Talby99, A. A. Talyshev120b,120a, J. Tanaka161, M. Tanaka163, R. Tanaka129, S. Tanaka79, R. Tanioka80, 
B. B. Tannenwald123, S. Tapia Araya144b, S. Tapprogge97, S. Tarem158, G. F. Tartarelli66a,P. Tas140,M. Tasevsky138, 
T. Tashiro83, E. Tassi40b,40a, A. Tavares Delgado137a,137b, Y. Tayalati34e, A. C. Taylor116, A. J. Taylor48, G. N. Taylor102, 
P. T. E. Taylor102, W. Taylor166b, P. Teixeira-Dias91, D. Temple149,H. Ten Kate35, P. K. Teng155, J.J.Teoh130, F. Tepel180, 
S. Terada79, K. Terashi161,J. Terron96, S. Terzo14,M. Testa49, R. J. Teuscher165,af, T. Theveneaux-Pelzer99, F. Thiele39, 
J. P. Thomas21, J. Thomas-Wilsker91, A. S. Thompson55, P. D. Thompson21, L. A. Thomsen181, E. Thomson134, 
M. J. Tibbetts18,R. E. Ticse Torres99, V. O. Tikhomirov108,ap, Yu. A. Tikhonov120b,120a, S. Timoshenko110, P. Tipton181, 
S. Tisserant99, K. Todome163, S. Todorova-Nova5, S. Todt46,J. Tojo85, S. Tokár28a, K. Tokushuku79, E. Tolley123, 
L. Tomlinson98, M. Tomoto115, L. Tompkins150,s,K. Toms116, B. Tong57, P. Tornambe50, E. Torrence128,H. Torres46, 
E. Torró Pastor145, J. Toth99,ae, F. Touchard99, D. R. Tovey146, C. J. Treado122, T. Trefzger175, F. Tresoldi153, 
A. Tricoli29, I. M. Trigger166a, S. Trincaz-Duvoid133, M. F. Tripiana14, W. Trischuk165, B. Trocmé56, A. Trofymov44, 
C. Troncon66a, M. Trottier-McDonald18, M. Trovatelli174, L. Truong32b, M. Trzebinski82, A. Trzupek82, K.W.Tsang61a, 
J. C-L. Tseng132, P. V. Tsiareshka105, G. Tsipolitis10, N. Tsirintanis9, S. Tsiskaridze14, V. Tsiskaridze50, 
E. G. Tskhadadze157a,K. M. Tsui61a, I. I. Tsukerman109, V. Tsulaia18, S. Tsuno79, D. Tsybychev152,Y. Tu61b, 
A. Tudorache27b, V. Tudorache27b,T. T. Tulbure27a, A.N.Tuna57, S. A. Tupputi23a,23b, S. Turchikhin77, D. Turgeman178, 
I. Turk Cakir4b,w, R. Turra66a, P.M.Tuts38, G. Ucchielli23b,23a, I. Ueda79, M. Ughetto43a,43b,F. Ukegawa167, G. Unal35, 
A. Undrus29, G. Unel169, F. C. Ungaro102, Y. Unno79, C. Unverdorben112, J. Urban28b, P. Urquijo102, P. Urrejola97, 
G. Usai8,J. Usui79, L. Vacavant99, V. Vacek139, B. Vachon101, K.O.H.Vadla131, A. Vaidya92, C. Valderanis112, 
E. Valdes Santurio43a,43b, M. Valente52, S. Valentinetti23b,23a, A. Valero172, L. Valéry14, S. Valkar140, A. Vallier5, 
J. A. Valls Ferrer172, W. Van Den Wollenberg118, H. Van der Graaf118, P. Van Gemmeren6, J. Van Nieuwkoop149, 
I. Van Vulpen118, M. C. van Woerden118, M. Vanadia71a,71b, W. Vandelli35, A. Vaniachine164, P. Vankov118, 
G. Vardanyan182,R. Vari70a, E. W. Varnes7, C. Varni53b,53a, T. Varol41, D. Varouchas129, A. Vartapetian8,K. E. Varvell154, 
G. A. Vasquez144b, J. G. Vasquez181, F. Vazeille37, T. Vazquez Schroeder101, J. Veatch51, V. Veeraraghavan7, 
L. M. Veloce165, F. Veloso137a,137c, S. Veneziano70a, A. Ventura65a,65b, M. Venturi174, N. Venturi35, A. Venturini26, 
V. Vercesi68a, M. Verducci72a,72b, W. Verkerke118, A. T. Vermeulen118, J. C. Vermeulen118, M. C. Vetterli149,ax, 
N. Viaux Maira144b, O. Viazlo94, I. Vichou171,*, T. Vickey146, O.E.Vickey Boeriu146, G. H. A. Viehhauser132, 
S. Viel18, L. Vigani132, M. Villa23b,23a, M. Villaplana Perez66a,66b, E. Vilucchi49, M. G. Vincter33, V. B. Vinogradov77, 
A. Vishwakarma44, C. Vittori23b,23a, I. Vivarelli153, S. Vlachos10, M. Vogel180, P. Vokac139, G. Volpi14, 
H. von der Schmitt113, E. Von Toerne24, V. Vorobel140, K. Vorobev110,M. Vos172,R. Voss35, J. H. Vossebeld88, 
N. Vranjes16, M. Vranjes Milosavljevic16,V. Vrba139, M. Vreeswijk118, T. Šﬁligoj89, R. Vuillermet35, I. Vukotic36, 
T. Ženiš28a,L. Živkovi´c16, P. Wagner24, W. Wagner180, J. Wagner-Kuhr112, H. Wahlberg86, S. Wahrmund46, J. Walder87, 
R. Walker112, W. Walkowiak148, V. Wallangen43a,43b, C. Wang15c, C. Wang58b,e, F. Wang179, H. Wang18, H. Wang3, 
J. Wang154, J. Wang44, Q. Wang125, R. Wang6, S. M. Wang155, T. Wang38, W. Wang155,q, W. X. Wang58a,ag, Z. Wang58c, 
C. Wanotayaroj128, A. Warburton101,C. P. Ward31, D. R. Wardrope92, A. Washbrook48, P. M. Watkins21,A. T. Watson21, 
M. F. Watson21, G. Watts145, S. Watts98, B. M. Waugh92, A. F. Webb11, S. Webb97, M. S. Weber20, S. A. Weber33, 
S. W. Weber175, J.S.Webster6, A. R. Weidberg132, B. Weinert63, J. Weingarten51, M. Weirich97,C. Weiser50, 
H. Weits118, P. S. Wells35, T. Wenaus29, T. Wengler35, S. Wenig35,N. Wermes24, M. D. Werner76, P. Werner35, 
M. Wessels59a,T. D. Weston20, K. Whalen128, N. L. Whallon145, A.M.Wharton87, A. S. White103, A. White8, 
M. J. White1, R. White144b, D. Whiteson169, B. W. Whitmore87, F. J. Wickens141, W. Wiedenmann179, M. Wielers141, 
C. Wiglesworth39, L. A. M. Wiik-Fuchs50, A. Wildauer113, F. Wilk98, H. G. Wilkens35, H. H. Williams134, S. Williams31, 
C. Willis104, S. Willocq100, J. A. Wilson21, I. Wingerter-Seez5, E. Winkels153, F. Winklmeier128,O. J. Winston153, 
B. T. Winter24, M. Wittgen150, M. Wobisch93, T.M.H.Wolf118,R. Wolff99, M. W. Wolter82, H. Wolters137a,137c, 
V. W. S. Wong173,S. D. Worm21, B.K.Wosiek82, J. Wotschack35, K.W.Wo´zniak82,M. Wu36,S. L. Wu179,X. Wu52, 
Y. Wu103, T. R. Wyatt98, B. M. Wynne48, S. Xella39,Z. Xi103,L. Xia15b,D. Xu15a,L. Xu29,T. Xu142, B. Yabsley154, 
S. Yacoob32a, D. Yamaguchi163, Y. Yamaguchi163, A. Yamamoto79, S. Yamamoto161, T. Yamanaka161, F. Yamane80, 
M. Yamatani161,Y. Yamazaki80,Z. Yan25,H. J. Yang58c,58d,H.T.Yang18,Y. Yang155,Z. Yang17,W-M. Yao18,Y. C. Yap133, 
Y. Yasu79, E. Yatsenko5, K. H. Yau Wong24,J. Ye41,S. Ye29, I. Yeletskikh77, E. Yigitbasi25, E. Yildirim97, K. Yorita177, 
K. Yoshihara134, C. J. S. Young35, C. Young150,J. Yu8,J. Yu76, S.P.Y.Yuen24, I. Yusuff31,a, B. Zabinski82, G. Zacharis10, 
R. Zaidan14, A. M. Zaitsev121,ao, N. Zakharchuk44, J. Zalieckas17, A. Zaman152, S. Zambito57, D. Zanzi102, C. Zeitnitz180, 
G. Zemaityte132,A. Zemla81a, J. C. Zeng171, Q. Zeng150, O. Zenin121,D. Zerwas129, D. Zhang103, F. Zhang179, 
G. Zhang58a,ag, H. Zhang15c, J. Zhang6, L. Zhang50, L. Zhang58a, M. Zhang171, P. Zhang15c, R. Zhang58a,e, R. Zhang24, 
X. Zhang58b,Y. Zhang15d,Z. Zhang129,X. Zhao41,Y. Zhao58b,129,ak,Z. Zhao58a,A. Zhemchugov77,B. Zhou103,C. Zhou179, 
L. Zhou41, M.S.Zhou15d, M. Zhou152, N. Zhou15b,C. G. Zhu58b,H. Zhu15a,J. Zhu103,Y. Zhu58a, X. Zhuang15a, 
123 


K. Zhukov108, A. Zibell175, D. Zieminska63, N.I.Zimine77, C. Zimmermann97, S. Zimmermann50, Z. Zinonos113, 
M. Zinser97, M. Ziolkowski148, G. Zobernig179, A. Zoccoli23b,23a,R. Zou36, M. Zur Nedden19, L. Zwalinski35 
1 Department of Physics, University of Adelaide, Adelaide, Australia 2 Physics Department, SUNY Albany, Albany, NY, USA 3 Department of Physics, University of Alberta, Edmonton, AB, Canada 4 a Department of Physics, Ankara University, Ankara, Turkey; b Istanbul Aydin University, Istanbul, Turkey; c Division 
of Physics, TOBB University of Economics and Technology, Ankara, Turkey 
5 LAPP, Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS/IN2P3, Annecy, France 
6 High Energy Physics Division, Argonne National Laboratory, Argonne, IL, USA 
7 Department of Physics, University of Arizona, Tucson, AZ, USA 
8 Department of Physics, University of Texas at Arlington, Arlington, TX, USA 
9 Physics Department, National and Kapodistrian University of Athens, Athens, Greece 

10 Physics Department, National Technical University of Athens, Zografou, Greece 
11 Department of Physics, University of Texas at Austin, Austin, TX, USA 
12 a Faculty of Engineering and Natural Sciences, Bahcesehir University, Istanbul, Turkey; b Faculty of Engineering and Natural Sciences, Istanbul Bilgi University, Istanbul, Turkey; c Department of Physics, Bogazici University, Istanbul, 
Turkey; d Department of Physics Engineering, Gaziantep University, Gaziantep, Turkey 
13 Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan 
14 Institut de Física d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona, Spain 
15 a Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China; b Physics Department, Tsinghua 
University, Beijing, China; c Department of Physics, Nanjing University, Nanjing, China; d University of Chinese 
Academy of Science (UCAS), Beijing, China 

16 Institute of Physics, University of Belgrade, Belgrade, Serbia 
17 Department for Physics and Technology, University of Bergen, Bergen, Norway 
18 Physics Division, Lawrence Berkeley National Laboratory and University of California, Berkeley, CA, USA 
19 Institut für Physik, Humboldt Universität zu Berlin, Berlin, Germany 
20 Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University of Bern, Bern, 
Switzerland 

21 School of Physics and Astronomy, University of Birmingham, Birmingham, UK 
22 Centro de Investigaciónes, Universidad Antonio Nariño, Bogotá, Colombia 
23 a Dipartimento di Fisica e Astronomia, Università di Bologna, Bologna, Italy; b INFN Sezione di Bologna, Bologna, 
Italy 
24 Physikalisches Institut, Universität Bonn, Bonn, Germany 

25 Department of Physics, Boston University, Boston, MA, USA 
26 Department of Physics, Brandeis University, Waltham, MA, USA 
27 a Transilvania University of Brasov, Brasov, Romania; b Horia Hulubei National Institute of Physics and Nuclear 
Engineering, Bucharest, Romania; c Department of Physics, Alexandru Ioan Cuza University of Iasi, Iasi, Romania; d Physics Department, National Institute for Research and Development of Isotopic and Molecular Technologies, Cluj-Napoca, Romania; e University Politehnica Bucharest, Bucharest, Romania; f West University in Timisoara, Timisoara, Romania 
28 a Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovak Republic; b Department of 
Subnuclear Physics, Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice, Slovak Republic 
29 Physics Department, Brookhaven National Laboratory, Upton, NY, USA 
30 Departamento de Física, Universidad de Buenos Aires, Buenos Aires, Argentina 
31 Cavendish Laboratory, University of Cambridge, Cambridge, UK 
32 a Department of Physics, University of Cape Town, Cape Town, South Africa; b Department of Mechanical 
Engineering Science, University of Johannesburg, Johannesburg, South Africa; c School of Physics, University of the 
Witwatersrand, Johannesburg, South Africa 

33 Department of Physics, Carleton University, Ottawa, ON, Canada 
34 a Faculté des Sciences Ain Chock, Réseau Universitaire de Physique des Hautes Energies-Université Hassan II, 
Casablanca, Morocco; b Centre National de l’Energie des Sciences Techniques Nucleaires (CNESTEN), Rabat, 
Morocco; c Faculté des Sciences Semlalia, Université Cadi Ayyad, LPHEA, Marrakech, Morocco; d Faculté des 
123 


Sciences, Université Mohamed Premier and LPTPM, Oujda, Morocco; e Faculté des sciences, Université Mohammed V, 
Rabat, Morocco 
35 CERN, Geneva, Switzerland 

36 Enrico Fermi Institute, University of Chicago, Chicago, IL, USA 
37 LPC, Université Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand, France 
38 Nevis Laboratory, Columbia University, Irvington, NY, USA 
39 Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark 
40 a Dipartimento di Fisica, Università della Calabria, Rende, Italy; b INFN Gruppo Collegato di Cosenza, Laboratori Nazionali di Frascati, Frascati, Italy 
41 Physics Department, Southern Methodist University, Dallas, TX, USA 
42 Physics Department, University of Texas at Dallas, Richardson, TX, USA 
43 a Department of Physics, Stockholm University, Stockholm, Sweden; b Oskar Klein Centre, Stockholm, Sweden 
44 Deutsches Elektronen-Synchrotron DESY, Hamburg and Zeuthen, Germany 
45 Lehrstuhl für Experimentelle Physik IV, Technische Universität Dortmund, Dortmund, Germany 
46 Institut für Kern-und Teilchenphysik, Technische Universität Dresden, Dresden, Germany 
47 Department of Physics, Duke University, Durham, NC, USA 
48 SUPA-School of Physics and Astronomy, University of Edinburgh, Edinburgh, UK 
49 INFN e Laboratori Nazionali di Frascati, Frascati, Italy 

50 Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany 
51 II Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen, Germany 
52 Département de Physique Nucléaire et Corpusculaire, Université de Genève, Geneva, Switzerland 
53 a Dipartimento di Fisica, Università di Genova, Genoa, Italy; b INFN Sezione di Genova, Genoa, Italy 
54 II. Physikalisches Institut, Justus-Liebig-Universität Giessen, Giessen, Germany 
55 SUPA-School of Physics and Astronomy, University of Glasgow, Glasgow, UK 
56 LPSC, Université Grenoble Alpes, CNRS/IN2P3, Grenoble INP, Grenoble, France 
57 Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge, MA, USA 
58 a Department of Modern Physics and State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China, Hefei, China; b Institute of Frontier and Interdisciplinary Science and Key Laboratory of Particle Physics and Particle Irradiation (MOE), Shandong University, Qingdao, China; c School of Physics and Astronomy, Shanghai Jiao Tong University, KLPPAC-MoE, SKLPPC, Shanghai, China; d Tsung-Dao Lee Institute, Shanghai, China 
59 a Kirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany; b Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany 
60 Faculty of Applied Information Science, Hiroshima Institute of Technology, Hiroshima, Japan 
61 a Department of Physics, Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China; b Department of Physics, University of Hong Kong, Hong Kong, China; c Department of Physics and Institute for Advanced Study, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China 
62 Department of Physics, National Tsing Hua University, Hsinchu, Taiwan 
63 Department of Physics, Indiana University, Bloomington, IN, USA 
64 a INFN Gruppo Collegato di Udine, Sezione di Trieste, Udine, Italy; b ICTP, Trieste, Italy; c Dipartimento di Chimica, Fisica e Ambiente, Università di Udine, Udine, Italy 
65 a INFN Sezione di Lecce, Lecce, Italy; b Dipartimento di Matematica e Fisica, Università del Salento, Lecce, Italy 
66 a INFN Sezione di Milano, Milan, Italy; b Dipartimento di Fisica, Università di Milano, Milan, Italy 
67 a INFN Sezione di Napoli, Naples, Italy; b Dipartimento di Fisica, Università di Napoli, Naples, Italy 
68 a INFN Sezione di Pavia, Pavia, Italy; b Dipartimento di Fisica, Università di Pavia, Pavia, Italy 
69 a INFN Sezione di Pisa, Pisa, Italy; b Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa, Italy 
70 a INFN Sezione di Roma, Rome, Italy; b Dipartimento di Fisica, Sapienza Università di Roma, Rome, Italy 
71 a INFN Sezione di Roma Tor Vergata, Rome, Italy; b Dipartimento di Fisica, Università di Roma Tor Vergata, Rome, Italy 
72 a INFN Sezione di Roma Tre, Rome, Italy; b Dipartimento di Matematica e Fisica, Università Roma Tre, Rome, Italy 
73 a INFN-TIFPA, Trento, Italy; b Università degli Studi di Trento, Trento, Italy 
74 Institut für Astro-und Teilchenphysik, Leopold-Franzens-Universität, Innsbruck, Austria 
75 University of Iowa, Iowa City, IA, USA 
123 


76 Department of Physics and Astronomy, Iowa State University, Ames, IA, USA 
77 Joint Institute for Nuclear Research, Dubna, Russia 
78 a Departamento de Engenharia Elétrica, Universidade Federal de Juiz de Fora (UFJF), Juiz de Fora, 

Brazil; b Universidade Federal do Rio De Janeiro COPPE/EE/IF, Rio de Janeiro, Brazil; c Universidade Federal de São 
João del Rei (UFSJ), São João del Rei, Brazil; d Instituto de Física, Universidade de São Paulo, São Paulo, Brazil 
79 KEK, High Energy Accelerator Research Organization, Tsukuba, Japan 
80 Graduate School of Science, Kobe University, Kobe, Japan 
81 a Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Kraków, 

Poland; b Marian Smoluchowski Institute of Physics, Jagiellonian University, Kraków, Poland 82 Institute of Nuclear Physics Polish Academy of Sciences, Kraków, Poland 83 Faculty of Science, Kyoto University, Kyoto, Japan 84 Kyoto University of Education, Kyoto, Japan 85 Research Center for Advanced Particle Physics and Department of Physics, Kyushu University, Fukuoka, Japan 86 Instituto de Física La Plata, Universidad Nacional de La Plata and CONICET, La Plata, Argentina 87 Physics Department, Lancaster University, Lancaster, UK 88 Oliver Lodge Laboratory, University of Liverpool, Liverpool, UK 89 Department of Experimental Particle Physics, Jožef Stefan Institute and Department of Physics, University of Ljubljana, 
Ljubljana, Slovenia 
90 School of Physics and Astronomy, Queen Mary University of London, London, UK 
91 Department of Physics, Royal Holloway University of London, Egham, UK 
92 Department of Physics and Astronomy, University College London, London, UK 
93 Louisiana Tech University, Ruston, LA, USA 
94 Fysiska institutionen, Lunds universitet, Lund, Sweden 
95 Centre de Calcul de l’Institut National de Physique Nucléaire et de Physique des Particules (IN2P3), Villeurbanne, 

France 
96 Departamento de Física Teorica C-15 and CIAFF, Universidad Autónoma de Madrid, Madrid, Spain 
97 Institut für Physik, Universität Mainz, Mainz, Germany 
98 School of Physics and Astronomy, University of Manchester, Manchester, UK 
99 CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille, France 

100 Department of Physics, University of Massachusetts, Amherst, MA, USA 
101 Department of Physics, McGill University, Montreal, QC, Canada 
102 School of Physics, University of Melbourne, Melbourne, VIC, Australia 
103 Department of Physics, University of Michigan, Ann Arbor, MI, USA 
104 Department of Physics and Astronomy, Michigan State University, East Lansing, MI, USA 
105 B.I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk, Belarus 
106 Research Institute for Nuclear Problems of Byelorussian State University, Minsk, Belarus 
107 Group of Particle Physics, University of Montreal, Montreal, QC, Canada 
108 P.N. Lebedev Physical Institute of the Russian Academy of Sciences, Moscow, Russia 
109 Institute for Theoretical and Experimental Physics (ITEP), Moscow, Russia 
110 National Research Nuclear University MEPhI, Moscow, Russia 
111 D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Moscow, Russia 
112 Fakultät für Physik, Ludwig-Maximilians-Universität München, Munich, Germany 
113 Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), Munich, Germany 
114 Nagasaki Institute of Applied Science, Nagasaki, Japan 
115 Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya, Japan 
116 Department of Physics and Astronomy, University of New Mexico, Albuquerque, NM, USA 
117 Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen, The Netherlands 
118 Nikhef National Institute for Subatomic Physics, University of Amsterdam, Amsterdam, The Netherlands 
119 Department of Physics, Northern Illinois University, DeKalb, IL, USA 
120 a Budker Institute of Nuclear Physics, SB RAS, Novosibirsk, Russia; b Novosibirsk State University, Novosibirsk, Russia 
121 Institute for High Energy Physics of the National Research Centre Kurchatov Institute, Protvino, Russia 
123 


122 Department of Physics, New York University, New York, NY, USA 
123 Ohio State University, Columbus, OH, USA 
124 Faculty of Science, Okayama University, Okayama, Japan 
125 Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman, OK, USA 
126 Department of Physics, Oklahoma State University, Stillwater, OK, USA 
127 RCPTM, Joint Laboratory of Optics, Palacký University, Olomouc, Czech Republic 
128 Center for High Energy Physics, University of Oregon, Eugene, OR, USA 
129 LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay, France 
130 Graduate School of Science, Osaka University, Osaka, Japan 
131 Department of Physics, University of Oslo, Oslo, Norway 
132 Department of Physics, Oxford University, Oxford, UK 
133 LPNHE, Sorbonne Université, Paris Diderot Sorbonne Paris Cité, CNRS/IN2P3, Paris, France 
134 Department of Physics, University of Pennsylvania, Philadelphia, PA, USA 
135 Konstantinov Nuclear Physics Institute of National Research Centre “Kurchatov Institute”, PNPI, St. Petersburg, Russia 
136 Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA, USA 
137 a Laboratório de Instrumentação e Física Experimental de Partículas-LIP, Lisbon, Portugal; b Departamento de Física, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal; c Departamento de Física, Universidade de Coimbra, Coimbra, Portugal; d Centro de Física Nuclear da Universidade de Lisboa, Lisbon, Portugal; e Departamento de Física, Universidade do Minho, Braga, Portugal; f Departamento de Física Teorica y del Cosmos, Universidad de Granada, Granada, Spain; g Dep Física and CEFITEC of Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal 
138 Institute of Physics, Academy of Sciences of the Czech Republic, Prague, Czech Republic 
139 Czech Technical University in Prague, Prague, Czech Republic 
140 Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic 
141 Particle Physics Department, Rutherford Appleton Laboratory, Didcot, UK 
142 IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France 
143 Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz, CA, USA 
144 a Departamento de Física, Pontiﬁcia Universidad Católica de Chile, Santiago, Chile; b Departamento de Física, Universidad Técnica Federico Santa María, Valparaiso, Chile 
145 Department of Physics, University of Washington, Seattle, WA, USA 
146 Department of Physics and Astronomy, University of Shefﬁeld, Shefﬁeld, UK 
147 Department of Physics, Shinshu University, Nagano, Japan 
148 Department Physik, Universität Siegen, Siegen, Germany 
149 Department of Physics, Simon Fraser University, Burnaby, BC, Canada 
150 SLAC National Accelerator Laboratory, Stanford, CA, USA 
151 Physics Department, Royal Institute of Technology, Stockholm, Sweden 
152 Departments of Physics and Astronomy, Stony Brook University, Stony Brook, NY, USA 
153 Department of Physics and Astronomy, University of Sussex, Brighton, UK 
154 School of Physics, University of Sydney, Sydney, Australia 
155 Institute of Physics, Academia Sinica, Taipei, Taiwan 
156 Academia Sinica Grid Computing, Institute of Physics, Academia Sinica, Taipei, Taiwan 
157 a E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi, Georgia; b High Energy Physics Institute, Tbilisi State University, Tbilisi, Georgia 
158 Department of Physics, Technion: Israel Institute of Technology, Haifa, Israel 
159 Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel 
160 Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece 
161 International Center for Elementary Particle Physics and Department of Physics, University of Tokyo, Tokyo, Japan 
162 Graduate School of Science and Technology, Tokyo Metropolitan University, Tokyo, Japan 
163 Department of Physics, Tokyo Institute of Technology, Tokyo, Japan 
164 Tomsk State University, Tomsk, Russia 
165 Department of Physics, University of Toronto, Toronto, ON, Canada 
166 a TRIUMF, Vancouver, BC, Canada; b Department of Physics and Astronomy, York University, Toronto, ON, Canada 
123 


167 Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Japan 
168 Department of Physics and Astronomy, Tufts University, Medford, MA, USA 
169 Department of Physics and Astronomy, University of California Irvine, Irvine, CA, USA 
170 Department of Physics and Astronomy, University of Uppsala, Uppsala, Sweden 
171 Department of Physics, University of Illinois, Urbana, IL, USA 
172 Instituto de Física Corpuscular (IFIC), Centro Mixto Universidad de Valencia-CSIC, Valencia, Spain 
173 Department of Physics, University of British Columbia, Vancouver, BC, Canada 
174 Department of Physics and Astronomy, University of Victoria, Victoria, BC, Canada 
175 Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany 
176 Department of Physics, University of Warwick, Coventry, UK 
177 Waseda University, Tokyo, Japan 
178 Department of Particle Physics, Weizmann Institute of Science, Rehovot, Israel 
179 Department of Physics, University of Wisconsin, Madison, WI, USA 
180 Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal, Wuppertal, Germany 
181 Department of Physics, Yale University, New Haven, CT, USA 
182 Yerevan Physics Institute, Yerevan, Armenia 
a Also at Department of Physics, University of Malaya, Kuala Lumpur, Malaysia 
b Also at Borough of Manhattan Community College, City University of New York, NY, USA 
c Also at Centre for High Performance Computing, CSIR Campus, Rosebank, Cape Town, South Africa 
d Also at CERN, Geneva, Switzerland 
e Also at CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille, France 
f Also at Département de Physique Nucléaire et Corpusculaire, Université de Genève, Geneva, Switzerland 
g Also at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona, Spain 
h Also at Departamento de Física Teorica y del Cosmos, Universidad de Granada, Granada, Spain 
i Also at Departamento de Física, Pontiﬁcia Universidad Católica de Chile, Santiago, Chile 
j Also at Departamento de Física, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal 
k Also at Department of Applied Physics and Astronomy, University of Sharjah, Sharjah, UAE 
l Also at Department of Financial and Management Engineering, University of the Aegean, Chios, Greece 

m Also at Department of Physics and Astronomy, University of Louisville, Louisville, KY, USA 
n Also at Department of Physics, California State University, Fresno, CA, USA 
o Also at Department of Physics, California State University, Sacramento, CA, USA 
p Also at Department of Physics, King’s College London, London, UK 
q Also at Department of Physics, Nanjing University, Nanjing, China 
r Also at Department of Physics, St. Petersburg State Polytechnical University, St. Petersburg, Russia 
s Also at Department of Physics, Stanford University, USA 
t Also at Department of Physics, University of Fribourg, Fribourg, Switzerland 
u Also at Department of Physics, University of Michigan, Ann Arbor, MI, USA 
v Also at Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa, Italy 

w Also at Giresun University, Faculty of Engineering, Giresun, Turkey 
x Also at Graduate School of Science, Osaka University, Osaka, Japan 
y Also at Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania 
z Also at II Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen, Germany 

aa Also at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona, Spain ab Also at Institut de Física d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona, Spain ac Also at Institut für Experimentalphysik, Universität Hamburg, Hamburg, Germany ad Also at Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen, 
The Netherlands 
ae Also at Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Budapest, Hungary 
af Also at Institute of Particle Physics (IPP), Canada 
ag Also at Institute of Physics, Academia Sinica, Taipei, Taiwan 

123 


ah Also at Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan ai Also at Institute of Theoretical Physics, Ilia State University, Tbilisi, Georgia aj Also at Instituto de Física Teórica de la Universidad Autónoma de Madrid, Spain ak Also at LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay, France al Also at Louisiana Tech University, Ruston, LA, USA 
am Also at LPNHE, Sorbonne Université, Paris Diderot Sorbonne Paris Cité, CNRS/IN2P3, Paris, France an Also at Manhattan College, New York, NY, USA ao Also at Moscow Institute of Physics and Technology State University, Dolgoprudny, Russia ap Also at National Research Nuclear University MEPhI, Moscow, Russia aq Also at Novosibirsk State University, Novosibirsk, Russia ar Also at Ochadai Academic Production, Ochanomizu University, Tokyo, Japan as Also at Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany at Also at School of Physics, Sun Yat-sen University, Guangzhou, China au Also at The City College of New York, New York, NY, USA av Also at The Collaborative Innovation Center of Quantum Matter (CICQM), Beijing, China 
aw Also at Tomsk State University, Tomsk, and Moscow Institute of Physics and Technology State University, Dolgoprudny, 
Russia ax Also at TRIUMF, Vancouver, BC, Canada ay Also at Universita di Napoli Parthenope, Napoli, Italy 
∗ Deceased 
123