A&A 566, A108 (2014) AstronomyDOI: 10.1051/0004-6361/201321489 
&
 cESO 2014 
Astrophysics 

The VIMOS Public Extragalactic Redshift Survey(VIPERS)? 
An unprecedentedviewof galaxiesandlarge-scale structureat0.5< z < 1.2 
L. Guzzo1,2, M. Scodeggio3, B. Garilli3,4, B. R. Granett1, A. Fritz3, U. Abbas5, C. Adami4, S. Arnouts4,6, J. Bel7,1, 
M. Bolzonella8, D. Bottini3, E. Branchini9,26,27, A. Cappi8,28, J. Coupon11,29, O. Cucciati8,16, I. Davidzon8,16, 
G.De Lucia12,S.delaTorre13,P. Franzetti3,M. Fumana3,P. Hudelot18,O. Ilbert4,A.Iovino1,J. Krywult14, 
V.Le 
Brun4,O.Le Fèvre4,D. Maccagni3,K. Ma ek15,F. Marulli16,17,8,H.J. McCracken18,L.Paioro3,J.A. Peacock13, 

M. 
Polletta3, A. Pollo20,21, H. Schlagenhaufer22,19,L.A.M.Tasca4,R.Tojeiro10,D.Vergani23,G. Zamorani8, 

A. 
Zanichelli24, A. Burden10, C. Di Porto8, A. Marchetti1,25,C. Marinoni7,Y. Mellier18,L. Moscardini16,17,8, 


R.C. Nichol10,W.J. Percival10,S. Phleps19, andM.Wolk18 
(Affiliations can be found after the references) 
Received 16 March 2013 / Accepted 10 March 2014 
ABSTRACT 
We describe the construction and general features of VIPERS, the VIMOS Public Extragalactic Redshift Survey. This ESO Large Programme is using theVery LargeTelescope with the aimofbuildinga spectroscopic sampleof ∼100000galaxies with iAB < 22.5and0.5 < z < 1.5. The survey covers a total area of ∼24 deg2 within the CFHTLS-Wide W1 and W4 felds. VIPERS is designed to address a broad range of problems in large-scale structureandgalaxyevolution, thankstoaunique combinationofvolume(∼5×107 h−3 Mpc3)and sampling rate(∼40%), comparable to state-of-the-art surveysofthe localUniverse, togetherwithextensive multi-band opticaland near-infrared photometry.Herewe presentthe survey design, the selectionof the source catalogue and thedevelopmentof the spectroscopic observations.We discussin detail theoverall selection function that results from the combination of the different constituents of the project. This includes the masks arising from the parent photometric sample and the spectroscopic instrumental footprint, together with the weights needed to account for the sampling and the success rates of the observations.Usingthe catalogueof53608galaxy redshifts composingthe forthcoming VIPERS PublicData Release1(PDR-1),weprovidea frst assessment of the quality of the spectroscopic data. The stellar contamination is found to be only 3.2%, endorsing the quality of the star–galaxy separation process and fully confrming the original estimates based on the VVDS data, which also indicate agalaxy incompleteness from this processofonly1.4%.Usingasetof1215repeatedobservations,we estimateanrms redshift error σz/(1+ z)= 4.7×10−4 and calibrate the internal spectral quality grading. Benefting from the combinationof size and detailed samplingof this dataset, we concludeby presentinga map showing in unprecedented detail the large-scale distributionofgalaxies between5and8billion years ago. 
Keywords. cosmology: observations – large-scale structureof Universe –galaxies: distances and redshifts–galaxies: statistics 
1. Introduction 
One of the major achievements of observational cosmology in the 20th century has been the detailed reconstruction of the large-scale structure of what is now called the “local Universe” (z ≤ 0.2). Large redshift surveys such as the 2dFGRS(Colless et al. 2001)and SDSS(York et al. 2000;Abazajian et al. 2009) have assembled samples of over a million objects, precisely characterising large-scale structure in the nearby Universe on scales ranging from 0.1 to 100 h−1Mpc. The SDSS in particular is stillextending its reach, using luminous redgalaxies (LRG) as 
? Based on observations collected at the European Southern Observatory, Cerro Paranal, Chile, using the Very Large Telescope under programmes 182.A-0886 and partly 070.A-9007. Also based on observations obtained with MegaPrime/MegaCam, a joint project of CFHT and CEA/DAPNIA, at the Canada-France-HawaiiTelescope (CFHT), whichis operatedbythe National Research Council(NRC)of Canada, the Institut National des Sciences de l’Univers of the Centre National de la Recherche Scientifque (CNRS) of France, and the University of Hawaii. This work is based in part on data products produced at TERAPIX and the Canadian Astronomy Data Centre as partof the Canada-France-HawaiiTelescopeLegacySurvey,a collaborative project of NRC and CNRS. The VIPERS website is http: <BR>//www.vipers.inaf.it/ <BR>
highly effective dilute tracersoflargevolumes(Eisensteinetal. 2011;Ahn et al. 2012). 
In addition to changing our view of thegalaxy distribution around us, the quantitative analysis of galaxy redshift surveys has consistently yielded important advances in our knowledge of the cosmological model. Galaxy clustering on large scales is one of the most important relics of the initial conditions that shaped our Universe, and the observed shape of the power spectrum P(k)of density fuctuations(orofitsFourier transform,the correlation function ξ(r)) indicates that we live in a low-density Universe in which only 25–30% of the mass-energy density is provided by (mostly dark) matter. Combined with other observations, particularly anisotropies in the cosmic microwave background (CMB), this observation has long argued for the rejectionof open modelsinfavourofa fatuniverse dominatedbya negative-pressure cosmological constant(Efstathiou et al. 1990). This conclusion predated the more direct demonstration via the Hubblediagramof distantTypeIaSupernovae(Riessetal.1998; Perlmutter et al. 1999)that the Universe is currently in a phase of accelerated expansion. Subsequent large-scale structure and CMB data (e.g. Cole et al. 2005;Komatsu et al. 2009;Hinshaw et al. 2013)haveonly reinforced the conclusion that the Universe is dominatedbya repulsive “dark energy”. Current observations 

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A&A 566, A108 (2014) 
are consistent with the latter being in the simplest form already suggested by Einstein with his Cosmological Constant, i.e. a fuid with non-evolving equation of state w = −1. 
Theoretical difficulties with the cosmological constant, specifcally the smallness and fne-tuning problems (e.g. Weinberg 1989)make scenarios with evolving dark energy an appealing alternative. This is the motivation for projects aiming at detecting a possible evolution of w(z). Redshift surveys are playing a crucial role in this endeavour, in particular after the discovery of the signature of baryonic acoustic oscillations (BAO)from the pre-recombination plasma into large-scale structure. This “standard rod” on a comoving scale of ∼150 Mpc (Percival et al. 2001; Cole et al. 2005; Eisenstein et al. 2005) provides us with a powerful mean to measure the expansion his-tory H(z)via the angular diameter distance (e.g.Percival et al. 2010;Blake et al. 2011a;Anderson et al. 2012). 
An even more radical explanation of the observed accel-erated expansion could be a breakdown of General Relativity (GR) on cosmological scales (see e.g. Carroll et al. 2004;Jain &Khoury 2010). Such a scenario is fully degenerate with dark energy in terms of H(z), a degeneracy that in principle can be lifted by measuring the growth rate of structure, which depends on the specifc theory describing gravity. 
There are in principle several experimental ways to mea-sure the growth of structure. Galaxy peculiar motions, in par-ticular, directly refect such growth. When the redshift is used as a distance proxy, theyproduce a measurable effect on cluster-ing measurements, what we call redshift-space distortions (RSD, Kaiser 1987). The anisotropy of statistical measurements like the two-point correlation function is proportional to the growth rate of cosmic structure f (z), which is a trademark of the gravity theory: if GR holds, we expect to measure a growth rate f (z)= [ΩM(z)]0.55(Peebles 1980;Lahavetal. 1991).Ifgravityis modifed on large scales, different forms are predicted (e.g. Dvali et al. 2000;Linder&Cahn 2007).Infact, although the RSD ef-fect has been well known since the late 1980s(Kaiser 1987), its potential in the context of dark energy and modifed gravity has become clear only recently(Guzzo et al. 2008;Zhang et al. 2007). The RSD method is now considered to be one of the most promising probes for future dark energy experiments, as testifed by the exponential growth in the number of works on both measurements (e.g. Beutler et al. 2012;Blake et al. 2011a;Reid et al. 2012), and theoretical modelling (e.g. Song & Percival 2009; Percival& White 2009; White et al. 2009; Scoccimarro 2004;Taruyaetal.2010;Kwanetal.2012;Reid&White2011; delaTorre&Guzzo 2012). Redshift surveys are thusexpected to be as important for cosmology in the present Century as they were in the previous one, as suggested by their central role in several planned experiments – especially the ESA dark-energy mission, Euclid (Laureijs et al. 2011). 
The scientifc yield of a redshift survey, however, extends well beyond fundamental cosmological aspects. It is equally im-portant to achieve an understanding of the relationship between the observed baryonic components in galaxies and the dark-matter haloes that host them.For this purpose, we need tobuild statistically complete samples ofgalaxies with measured positions, luminosity, spectral properties and (typically) colours and stellar masses; in providing such data, redshift surveys are thus avitalprobeofgalaxy formationandevolution. Signifcantstatistical progress has been made in relating thegalaxy distribution to the underlying dark matter, via “halo occupation distribution” (HOD) modelling(Seljak 2000;Peacock&Smith 2000; Cooray&Sheth2002),of accurate estimatesofthegalaxytwo-point correlation function, for samples selected in luminosity, colour and stellar mass (e.g. Zehavi et al. 2004). At the same time, important globalgalaxy population trendsinvolving properties such as luminosities, stellar masses, colours and structural parameters can be precisely measured when these parameters are available for ∼106 objects, as in the case of the SDSS (e.g. Kauffmann et al. 2003). 
In more recent years, deeper redshift surveys over areas of 1–2 deg2 have focused on exploring how this detailed picture emerged from the distant past. This was the direct consequence of the development during the 1990s of multi-object spectrographs on 8-m class telescopes. The most notable projects of this kind have been the VIMOS VLT Deep Survey (VVDS; Le Fèvre et al. 2005), the DEEP2 survey(Coil et al. 2008)and the zCOSMOS survey(Lilly et al. 2009), which adoptedvarious strategies aimed at covering an extended redshift range, up to z ∼ 4.5. Such depths inevitably limit the angular size and thus the volume explored in a given redshift interval, refecting the desire of these projects to tracegalaxy evolution back to its earliest phases, while understanding its relationship with environmentovera limited rangeof scales1.Evolutionary trends in the dark-matter/galaxy connection were explored using these surveys(Zheng et al. 2007; Abbas et al. 2010),but none of these samples had sufficient volume to produce stable and re-liable comparisons of e.g. the amplitude and shape of the correlation function.OnlytheWideextensionofVVDS(Garillietal. 2008), started to have sufficient volume as to attempt cosmologically meaningful computations at z ∼ 1(Guzzo et al. 2008), albeit with large error bars. In general, clustering measurements at z ∼ 1 from these samples remained dominated by feld-to-feld fuctuations (cosmic variance), as dramatically shown by the discrepancy observed between the VVDS and zCOSMOS correlation function estimates at z ' 0.8(delaTorreetal. 2010). 
Attheendofthepast decadeitwas thereforeclearthatanew step in deep redshift surveys was needed, if these were to pro-duce statistical results that could be compared on an equal footing with those derived from surveys of the local Universe, such as 2dFGRS and SDSS.Following thoseefforts, new generations of cosmological surveys have focused on covering the largest possiblevolumesat intermediate depths, utilizing relativelylowdensity tracers, with the main goal of measuring theBAO signal at redshifts 0.4–0.8. This is the case with the SDSS-3 BOSS project(Eisensteinetal.2011;Dawsonetal.2013), whichex<BR>tends the concept pioneeredby the SDSS selectionof LRG (e.g. Anderson et al. 2012;Reid et al. 2012). Similarly, theWiggleZ survey further exploited the long-lived 2dF positioner on the AAT 4-m telescope, to target emission-line galaxies selected from UV observations of the GALEX satellite(Drinkwater et al. 2010;Blake et al. 2011a,b). Both these surveys are characterised by a very large volume (1–2h−3Gpc3), and a relatively sparse galaxy population(∼10−4h3Mpc−3). This is typical of surveys performed with fbre positioning spectrograph, which normally can observe 500–1000 galaxies over areas of 1–2 square de-grees. Highergalaxy densities can be achieved with such systems via multiple visits, although this then limits the redshift 
1 The PRIMUS survey(Coiletal. 2011)isa notable recent addition, with ∼120000 spectraforgalaxiesat z < 1, collectedover7felds fora total areaof9deg2. Redshifts are obtained withalow-resolution prism (Cool et al. 2013), yielding typical errors one order of magnitude larger than those of the VIMOS surveys (see also Sect. 5.3).As such, analyses of these datahave concentrated ongalaxyevolution studies requiring lower precision ongalaxy distances. Nevertheless, while we were re-vising this paper,a frst detailed studyof the clusteringofgalaxies asa functionof luminosity and colourwas publishedin the arXiv(Skibba et al. 2014). 
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L. Guzzo et al.: The VIMOS Public Extragalactic Redshift Survey(VIPERS) 
and/or volume surveyed. This approach has been taken by the GAMA survey(Driver et al. 2011), which aims to achieve simi-lar numbers of redshifts to the 2dFGRS(∼200 000),butworking to r < 19.8and out toz ' 0.5. Indeed, the high sampling density of GAMA makes it an important intermediate step between the local surveys and the higher redshifts probed by the survey we are presenting in this paper, i.e. VIPERS. 
VIPERS stands for VIMOS Public Extragalactic Redshift Surveyand has been designed to measure redshifts for approximately100000galaxiesata median redshift z ' 0.8. The central goalof this strategyistobuilda data set capableof achieving an order of magnitude improvement on the key statistical de-scriptionsofthegalaxy distributionand internal properties,atan epoch when the Universe was about half its current age. Such a data set would allow combination with local samples on a com-parable statistical footing. Despite being centred at ¯z ∼ 0.7, in terms of volume and number density VIPERS is similar to lo-cal surveys like 2dFGRS and SDSS. All these surveys are char-acterised by a high sampling density, compared to the sparser samplesof the recent generationofBAO-oriented surveys. 
In this paper we provide an overview of the VIPERS survey design and strategy, discussing in some detail the construction of the target sample. The layout of the paper is as follows: in Sect. 2, we discuss the survey design; in Sect. 3 we describe the properties of the VIPERS parent photometric data and the build-upofa homogeneous sampleover24deg2;in Sect.4we discuss how from these data the specifc VIPERS target sample at z > 0.5has been selected, usinggalaxy colours;in Sect.5the details of the VIMOS observations and the general properties of the spectroscopic sample are presented;in Sect. 6we discussthe various selection effects and how theyhave been accounted for; fnally, in Sect. 7we present the redshift and large-scale spatial distribution of the current sample, summarising the scientifc in-vestigations that are part of separate papers currently submitted or in preparation. 
As a public survey, we hope and expect that the range of science that will emerge from VIPERS will greatly exceed the core analyses from the VIPERSTeam. This paper is therefore also to introduce the newVIPERS data, in viewof the frst Public Data Release (PDR-1)2, which is described in more detail in the specifc accompanying paper(Garilli et al. 2014). 


2. Surveydesign 
VIPERS was conceived in 2007 with a focus on clustering and RSD at z ' 0.5–1, but with a desire to enable broader goals involving large-scale structure andgalaxyevolution, similarly to the achievements of 2dFGRS and SDSS at z ' 0.1. The survey design was also strongly driven by the specifc features of the VIMOS spectrograph, which has a relatively small feld of view compared to fbre positioners('18 × 16arcmin2;see Sect. 6), but a larger yield in terms of redshifts per unit area. 
Given the luminosity function ofgalaxies and results from previous VIMOS surveys as VVDS(Le Fèvre et al. 2005;Garilli et al. 2008)and zCOSMOS(Lilly et al. 2009), we knew thata magnitude-limited sample with iAB < 22.5–23.0would cover the redshift range out to z ∼ 1.2, and couldbe assembled withfairly short VIMOSexposure times(<1h). Also, taking 2dFGRS as a local reference, a comparable surveyvolume ∼5× 107h−3Mpc3 could have been covered by mapping at this depth an area of ∼25 deg2. The frst attempt towards this kind of survey was VVDS-Wide, which covered ∼8 deg2 down to a magnitude 
Available athttp://vipers.inaf.it <BR>
iAB = 22.5,but observing all kinds of objects (stars andgalaxies), withlow sampling('20%). 
Building upon this experience, VIPERS was designed to maximise the number ofgalaxies observed in the range of in-terest, i.e. at z > 0.5, while at the same time attempting to select against stars, which represented a contamination up to 30% in some of the VVDS-Wide felds. The latter criterion re-quires multi-band photometric information and excellent seeing quality, but these qualities also beneft the galaxy sample, wherea wider rangeof ancillary scienceis enabledif thegalaxy surface-brightness profles can be well resolved. The outstanding imaging dataset that was available for these purposes was the Canada-France-HawaiiTelescopeLegacySurvey(CFHTLS) Wide photometric catalogue, as described belowin Sect.3. 
The desired redshift rangewas isolated througha simple and robust colour–colour selection on the(r − i)vs.(u − g)plane (as shownin Fig. 3). Thisis oneof many waysin which wehave been able to beneft from the experience of previous VIMOS spectroscopic surveys: we could be confdent in advance that this selection method would efficiently removegalaxies at z < 0.5, while yielding >98% completeness for z > 0.6, as verifed in the results shown below. A precise calibration of this separation method was made possible by the location of the VVDS-Wide(iAB < 22.5) and VVDS-Deep(iAB < 24) samples within the W4 and W1 felds of CFHTLS, respectively. This was an important reason for locating the VIPERS survey areas within these two CFHTLS felds while partly overlapping the original VVDS areas, as shownin Fig. 1. The magnitude limitwas set as in VVDS-Wide, i.e. 17.5 ≤ iAB ≤ 22.5 (after correction for Galactic extinction). 
The details of the star–galaxy separation are discussed in AppendixA, while the colour–colour selection is described in Sect.4. 


3. Photometric source catalogue 
The VIPERS target selection is derived from the ‘T0005’ release of the CFHTLSWide whichwasavailable for the frst observing season 2007/2008. This object selection was completed and improved using the subsequent T0006 release, as we describe in the following. 
The mean limiting AB magnitudes of CFHTLSWide (corresponding to the 50% completeness for point sources) are 
∗ 00 
∼25.3, 25.5, 24.8, 24.48, 23.60 in u ,g, r, i0 , z0, respectively.To construct the CFHTLS catalogues used here, objects in each tile were detected on a gri-χ2 image(Szalay et al. 1999)and galaxies were selected usingSEXtractor’s “mag_auto” mag-nitudes(Bertin&Arnouts 1996), in the AB system3. These are the magnitudes used throughout this work, after theyhave been corrected for foreground Galactic extinction using the following prescription: 
∗ 
u  =  u raw  − 4.716 ∗ E(B − V)  (1)  
g  =  0 g raw  − 3.654 ∗ E(B − V)  (2)  
r  =  0 rraw  − 2.691 ∗ E(B − V)  (3)  
i  =  i0 raw  − 1.998 ∗ E(B − V)  (4)  
z  =  0 zraw  − 1.530 ∗ E(B − V),  (5)  

where theextinctionfactor E(B − V)is derived at eachgalaxy’s position from the Schlegel dust maps(Schlegel et al. 1998). 
3 http://terapix.iap.fr/rubrique.php?id_rubrique=252 <BR>
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When the frst target catalogues were generated, the CFHTLS surveyincluded some photometrically incomplete ar-eas (“holes” hereafter). In these areas one or more bands was either corrupted or missing. In particular, all of the VIPERS W1 feld at right ascensions less than RA ' 02h090 were missing one band as CFHTLS Wide observations had not been completed. Smaller survey holes were mostly due to the partial failure of amplifer electronics (since all CCDs have two outputs, some images are missing only half-detector areas). 
In general, these missing bands meant that we were not able to select VIPERS targets in the affected areas and they were therefore excluded from our frst two observing seasons (2008 and 2009). The majority of these problems were fxed in Summer 2010 using the CFHTLS-T0006, which was carefully merged with the existing VIPERS target list. The T0005 and T0006 catalogs, limited to iAB < 23.0, were positionally matched over the area of each hole, using a search radius of 
0.6 arcsec. All matches witha compatible i-band magnitude (defned as having a difference less than 0.2 mag) were considered as good identifcations and used to verify the consistency be-tween the two releases. 
For objects near the VIPERS faint limit, i.e. iAB ∼ 22.5, the rms magnitude offset between the two catalogues was found to range between 0.02 to 0.04 mag (larger in the u-band), and smaller than this for brighter objects. Given this result, we con-cluded that the T0006 version of galaxy magnitudes could be used directly to replace the bad or missing magnitudes for the original T0005 objects in the holes. This solution was defnitely preferable to replacing all magnitudes with their T0006 values, an operation that would have modifed the target sample at the faint limit simply due to statistical scatter. 
Only a few of the T0005 holes arising from CCD failures were not flledby the T0006 release.To complete these remaining areas, Director’s Discretionary Time (DDT) was awarded at CFHT with MegaCam in summer 2009 (Arnouts& Guzzo, priv. comm.). At the end of 2010, the combination of new T0006 observations and the DDT data resulted in a virtually complete coverage in all fve bands of the two VIPERS areas in W1 and W4. The last problem to be resolved was re-calibrating a few small areas which were observed in T0006 with a new i-band flter, called “y”, as the original i-band flter broke in 2007. This procedureis describedin Appendix B. 

3.1. Tile-to-tile zero-point homogenisation 
The CFHTLS data are provided in single tiles of ∼1 deg side, overlapping each other by ∼2 arcmin to allow for cross-calibration. These areshowninFig. 1fortheW1andW4 felds, together withthe positionofthetwo VIPERS areas.Tobuildthe VIPERS global catalogue we merged adjacent tiles, eliminating duplicated objects. In these cases, the object in a pair having the bestTerapix fagwas chosen;if the fags were identical, the ob-ject at the greater distance from the tile borderwas chosen.Tiles were merged proceeding frst in right ascension rows and then merging the rows into a single catalogue. 
For anygalaxy surveyplanning to measure large-scale clustering it is crucial that the photometric or colour selection is as homogeneous as possible over the full survey area in order to avoid creating spurious object density fuctuations that could be mistaken as real inhomogeneities. Given the way the CFHTLS-Wide catalogue has been assembled, verifying and correcting anytile-to-tile variation of this kind is therefore of utmost im-portance. In fact, it was known and directly verifed that each tilein T0005 stillhada smallbut non-negligible zero-pointoff-set in some of the photometric bands. These offsets are a con-sequence of non-photometric images being used as photometric anchor felds in the global photometric solution. 
These tile-to-tile colourvariations areevident when stars are plottedina colour–colour diagram,asinFig. 2.In this fgurewe showthe(u−g)vs.(r−i)colours for stellar objects in two particularly discrepant tiles (see Appendix Afor details on how stars andgalaxies are separated). Suchoffsets can produce two kinds of systematic effectsina surveylike VIPERS. First,a tile-to-tile difference in the selection magnitude(i band) would introduce a varying surveydepth over the skyand thus a variation in the expected number counts and redshift distribution. Secondly, the colours would be affected, and thus anycolour–colour selection (as the one applied to selectgalaxies at z > 0.5for the VIPERS 
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L. Guzzo et al.: The VIMOS Public Extragalactic Redshift Survey(VIPERS) Fig. <BR>2. <BR>One of largest tile-to-tile magnitude zero-point variations in the T0005 data. The position of the stellar sequence in the(g <BR>− <BR>r) vs. (u <BR>− <BR>g)plane is compared for tile #9 and tile #11 in the W4 VIPERS area (see Appendix C), showing anoffset of ∼0.15 mag in(g <BR>− <BR>r)and ∼0.06 in(u <BR>− <BR>g)between the two tiles. target catalogue – see next section), would vary from one tile to another. The well-defned location of stars in colour–colour space, as showninFig. 2,suggestsa techniquefora possible correctionof the colour variations, i.e. using the observed stellar sequence as a colour calibrator (see High et al. 2009, for a similar more re-cent application of this regression technique). An important as-sumption of this correction procedure is that stars andgalaxies are affected by similar zero-point shifts, and thus that stellar se-quences can also be used to improve the photometric calibration of extended objects. This assumption is quite reasonable and it is the same adopted atTerapix in the past to check internal cal-ibration until the second-last release, i.e. T0006.With the latest release, T0007, there are indications that a contribution to these zero-point discrepancies could be also due to a dependence on seeing of mag_auto <BR>when applied to stellar objects. This effect is not fully understood yet and its amplitude is smaller than the corrections we originally applied to the T0005 data. The poten<BR>tial systematic impact of this uncertainty, in particular on clus<BR>tering analyses of the PDR-1 sample, is explicitly addressed in the corresponding papers(seee.g. delaTorreetal.2013). The colour corrections were carried out assuming (a) that the i-band magnitude had a negligible variation from tile to tile, and (b) taking the colours measured in tile W1-25 (see Fig. 1) as the reference ones. W1-25 is the tile overlapping the VVDS-Deep survey, which was used to calibrate the colour selection criteriaas discussedin Sect. 4.By referringall coloursto that tile, we assured (at least) that the colour-redshift correlation we calibratedwas applied self-consistently to all tiles.Forall tiles covered by VIPERS we measured therefore the(u <BR>− <BR>g)value of the blue-end cut-off <BR>in the stellar sequence, clearly visible in Fig.2,together with the zero points derived froma linear regres-sion to the(g <BR>− <BR>r)vs.(u <BR>− <BR>g)and(r <BR>− <BR>i)vs.(u <BR>− <BR>g)relationships for stars. These two regressions give a consistent slope of 0.50 and 0.23, respectively, over all tiles. This allowed us to compute three colouroffsets δug,δgr and δri for each tile, corresponding to the values required to match the same measurements in W1-25. Fig. <BR>3. <BR>Distribution in the(r <BR>− <BR>i)vs.(u <BR>− <BR>g)plane ofiAB <BR>< <BR>22.5galax<BR>ies with known redshift from the VVDS-Deep survey, showing the kind of selection applied to construct the VIPERS target sample. The colour selectionofEq.(9)is describedbythe continuous line, which empiri<BR>cally splits the sample into z <BR>> <BR>0.5(red flled circles) andz <BR>< <BR>0.5(blue open circles) by optimising the completeness and contamination of the high-redshift sample. All following stepsin the selectionof VIPERS targetgalax<BR>ies were then operated on colours corrected using these offsets, i.e. (u <BR>− <BR>g) = <BR>(u <BR>− <BR>g)uncorr − <BR>δug (6) (g <BR>− <BR>r) = <BR>(g <BR>− <BR>r)uncorr − <BR>δgr (7) (r <BR>− <BR>i) = <BR>(r <BR>− <BR>i)uncorr − <BR>δri. <BR>(8) 4. Selection of VIPERS galaxy targets Around halfof thegalaxiesina magnitude-limited sample with iAB <BR>< <BR>22.5 are at z <BR>< <BR>0.5. At the same time, the average num<BR>ber of slits that can be accommodated within one of the four VIMOS quadrants (see below) is approximately fxed, for a par-ent sample with a given depth and clustering. This means that in a pure magnitude limited surveywith iAB <BR>< <BR>22.5, around half of the slits would fall on z <BR>< <BR>0.5galaxies. Given the original goalof VIPERStobuilda sample complementaryto local sur-veys,a strategywasdevisedasto selecta priorionlygalaxies at higher redshifts, doubling in this way the sampling over the high-redshift range. Using available magnitude-limited VVDS data, a simple yet effective and robust colour selection criterion was devised through a series of experiments. The most effective criteria are shown in Fig. 3applied to the VVDS data. Galaxies are retained in the source list if their colours obeythe following relationship: (r <BR>− <BR>i)> <BR>0.5(u <BR>− <BR>g) OR (r <BR>− <BR>i)> <BR>0.7. <BR>(9) The resulting distribution of the true redshifts for the selected samplesisshowninFig. 4, withthe correspondinglevelof com-pleteness shown in Fig. 5. To compute the latter quantity, we usedtheVVDSdata(bothDeepandWide),andplotthe ratioof the numbers of objects in a VIPERS-like selected sample, to the original total redshift sample.We call this quantity the Colour Sampling Rate (CSR).As indicatedby the combinationof these A108, page5of 21 
Fig. <BR>4. <BR>Test of the colour–colour redshift selection, usinggalaxies with known redshift from the VVDS-Deep survey,limited to iAB < 22.5. The colourlocusinFig. 3isusedto separateapriorigalaxieslyingbelow (blue-dashed line) and above (solid red line) z ' 0.5. The dotted black line shows the global dN/dz of the sample. The VVDS-Deep sample hasbeen limitedtoobjects belongingtotile#25(wherethebulkofthe sample is concentrated), given that this has been used as the reference for the global colour calibration discussed in the text. 
two fgures, the VIPERS selection does not introduce any signifcant colour bias (i.e. it selects virtually allgalaxies) above z ∼ 0.6, with an acceptable contamination(∼5%) of low-redshift interlopers. More quantitatively, some insight on the potential incompleteness of the selection procedure – i.e. on how many galaxies that should have been included in our sample atz > 0.5 are lost – can be derived by looking in detail at the few outliers in the blue histogram of Fig. 4. The tail of unselected objects at z > 0.5 in the VVDS calibration sample includes 46 cases. 14of these are classifedas activegalactic nuclei(AGN)bythe VVDS, which explains whytheir colour–redshift relation does not match the standard criteria defned for galaxies; 10 out of the 14AGNs have z > 1.2and are thus out of the typical range usedbyVIPERS for statistical studies; thus only the remaining4 could potentially be part of the VIPERS target sample, although one cannot distinguish how much the active nucleus contributes to the overall magnitude (and thus, understand whether the ob-ject would be brighter than IAB = 22.5based on the sole magnitude of the hostgalaxy).7of the remaing outliers have an error on the u-band magnitude which is larger than 0.1 mag, which makes their u − g colour unreliable; another 3 have a redshift fag = 1, i.e. their redshift has a ∼50% probability to be wrong (seeSect. 5.3).Weareleftwith22 further outliers,amongwhich 8objects are atz > 1.2, while (after visual inspection) another 4 show noisy spectra suggesting an incorrect redshift, thus re-sulting in 10 furthergalaxies that apparently should have been included in the target sample. 
Based on these fgures, a conservative upper limit to the number ofgalaxies missed in the range0.5 < z < 1.2 for the VIPERS-like test sample can be obtained by summing up these numbers: 4 (AGN host galaxies), plus 3 (assuming conservatively that all Flag = 1redshifts are correct),7(wrong colour), plus 10 (remaininggalaxies with confrmed z > 0.5 redshifts). This corresponds to an estimated global incompleteness of the 
z > 0.5colour/redshift selection of 24/1068, i.e. 2.2%. It should 

Fig. <BR>5. <BR>Direct verifcation of the completeness of the VIPERS colour selection as a function of redshift, using both VVDS-Deep and VVDS-Wide data, in W1 and W4 respectively. Note that the original colour criteria were defned based only on the VVDS-Deep data. The curves and points give the Colour Sampling Rate (CSR), i.e. the ratio of the numberofgalaxies satisfying the VIPERS criteria withina redshift bin and the total number ofgalaxies in that same bin. Both felds provide consistent selection functions, indicating that the colour–colour selec<BR>tion function is basically unity above z <BR>= <BR>0.6 and can be consistently modelledin the transition region0.4< <BR>z <BR>< <BR>0.6. be noted also that, as visible in Fig. 4, a signifcant fraction of this incompleteness is concentrated in the transition region 0.5 < <BR>z <BR>< <BR>0.6and can be modelled as shown in Fig.5and dis-cussedin some detailin Garillietal. (2014). An alternative technique to select a high-redshift sample could have been to use photometric redshifts computed using allfve CFHTLS bands.Weverifed that this method provides comparable performance in terms of completeness and contam<BR>ination to the colour–colour selection. However we preferred a simple colour–colour criterion, as it can be reproduced precisely at anytime, while photometric redshifts depend inevitably also on the features of the specifc codes and template selection used, which will evolve with time. Finally, to further broaden the scientifc yield of VIPERS, thegalaxytarget cataloguewas supplementedwithtwosmallad<BR>ditional samplesofAGN candidates. These includea sampleof X-ray selectedAGNsfromthe XMM-LSSsurveyintheW1feld (Pierreetal.2007),andasampleof colour–defnedAGN can<BR>didates selected among objects classifed as stars in the previous phase. These two catalogues contributed on average 1–3 objects per quadrant (against about 90 galaxy targets) with negligible impact on thegalaxy selection function. TheseAGN candidates are excluded from the current PDR-1 sample. All the details on the selection criteria and the properties of the resulting objects will be discussed in a future paper. 5. VIMOS observations 5.1. The VIMOS spectrograph The VIPERS project is designed around the ESO VIsible Multi-Object Spectrograph (VIMOS), on “Melipal”, the ESO Very A108, page6of 21 

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Fig. <BR>6. <BR>Example of the detailed footprint and disposition of the four quadrants in a full VIMOS pointing (W1P082 in this case). Note the re-constructed boundaries (solid red lines), which have been traced pointing-by-pointing through an automatic detection algorithm that follows the borders of the illuminated area. These can vary in general among different pointings in the database, in particular due to the CCD refurbishment 
of 2010 and sometimes to vignettingby the telescope guide probe arm. 
LargeTelescope(VLT)Unit3(Le Fèvreetal.2003).VIMOSis a 4-channel imaging spectrograph; each channel (a “quadrant”) covers ∼7× 8arcmin2 for a total feld of view (a “pointing”) of ∼218 arcmin2.Each channelisa complete spectrographwiththe possibility to insert ∼30 × 30 cm2 slit masks at the entrance fo-cal plane, as well as broad-band flters or grisms. The standard layout of the four quadrants on the skyis reproduced in Fig. 6. The fgure shows the slit positions and the resulting location of the spectra, overlaid on the direct pre-image of pointing P082 in the W1 feld. 
The pixel scale on the CCD detectors is 0.205 arcsec/pixel, providingexcellent samplingof theParanal mean image quality and Nyquist sampling for a slit 0.5 arcsecond in width. For the VIPERS survey, we use slits of 1 arcsecond, together with the “low-resolution red” (LR-Red) grism, which providesa spectral resolution R ' 250 over the wavelength range ∼5500– 9500 Å. The instrument has no atmospheric dispersion compensator,giventhelargesizeofits feld-of-viewattheVLTNasmyth focus('1m).For this reason, observationshavetobe limitedto airmasses below 1.7.For VIPERS observations we rarely went above an airmass of 1.5. 
To prepare the MOS masks, directexposures (“pre-images”) need to be observed beforehand under the same instrumental conditions. Object positions in these images are then cross-correlated with the target catalogue in order match its astro-metric coordinates to the actual instrument coordinate system. This operation is performed during the mask preparation using VMMPS, the standard package for automatic optimisation of the positions and total number of slits(Bottini et al. 2005). 
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Fig. <BR>7. <BR>Afew representativeexamplesof VIPERS spectraof early-and late-typegalaxies, chosen among thedifferent quality classes (i.e. quality fags) and at different redshifts. The quoted fux is the observed one, without corrections for fnite-slit losses. The typical absorption and emission features are marked. 
In summer 2010, VIMOS was upgraded with new red-sensitive CCDs in each of the 4 channels, as well as with a new active fexure compensation system. The reliability of the mask exchange system was also improved(Hammersley et al. 2010). The original thinned E2V detectors were replaced by twice-thicker E2V devices, considerably lowering the fringing and increasing the global instrument efficiencybyup toafac-tor 2.5 (one magnitude) in the redder part of the wavelength range. This upgrade signifcantly improved the average quality of VIPERS spectra, resulting in a signifcantly higher redshift measurement success rate. 
5.2. Data reduction, redshift measurement and validation 
VIPERS is the frst VIMOS redshift surveyfor which the data reduction is performed with a fully automated pipeline, start-ing from the raw data and down to the calibrated spectra and redshift measurements. The pipeline includes and updates al-gorithms from the original VIPGI system (Scodeggio et al. 2005)withina complete purpose-built environment.Within it, the standard CCD data reduction, spectral extraction and cali-bration follow the usual recipes discussed in previous VIMOS papers(Le Fèvre et al. 2005;Lilly et al. 2009). The difference in the case of VIPERS is that the only operation for which we still require human interventionistheverifcationandvalidation of the measured redshift. All data reduction has been centralised in our data reduction and management centre at INAF – IASF Milano. When ready, the fully reduced data are made available to the team within a dedicated database. The full management of these operations within the “EasyLife” environment is de-scribedin Garillietal. (2008).Figure7showsafewexamplesof VIPERS spectra, forgalaxies withvarying redshift and quality fag. In common with previous VIMOS surveys (e.g. Le Fèvre et al. 2005;Lilly et al. 2009), all redshifts have been validated independently by two scientists but with some simplifcation to increase efficiency given the very large number of spectra. Nevertheless, this required a very strong team effort.Two team membersareassignedthe sameVIMOSfeldtoreview,withone of the two being the primary person responsible for that point-ing. At the end of the process discrepant redshifts resulting from the two reviewers are discussed and reconciled. 
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The quality of the measured redshifts is quantifed at the time of validation through a similar grading scheme to that described in Le Fèvre et al. (2005)andLilly et al. (2009). The correspond<BR>ing confdence levels are estimated from repeated observations, as explained in Sects. 5.3 and 5.4: 
– 
Flag 4.X:a high-confdence, highly secure redshift, based on a high signal-to-noise ratio(S/N)spectrum and supportedby obvious and consistent spectral features. The combined con-fdence level of Flag4 + Flag3measurements is estimated to be >99% 

– 
Flag 3.X: also a very secure redshift, comparable in confdence withFlag4, supportedby clear spectral featuresinthe spectrum,but not necessarily with highS/N. 

– 
Flag 2.X: a fairly secure, ∼95% confdence redshift mea-surement, with sufficient spectral features in support of the measurement. 

– 
Flag 1.X: a tentative redshift measurement, based on weak spectral features and/or continuum shape, for which there is ∼50% chance that the redshift is actually wrong. 

– 
Flag 0.X: no reliable spectroscopic redshift measurement was possible. 

– 
Flag 9.X: redshift is based on only one single clear spectral emission feature, usually identifed (in the VIPERS range) with [OII]3727 Å. 

– 
Flag –10: spectrum with clear problems in the observation or data processing phases.In most cases thisisafailedextractionby VIPGI(Scodeggioetal.2005)orabadskysubtraction because the object is too close to the edge of the slit. 


Serendipitous objects appearing by chance within the slit of the main target are identifed by adding a “2” in front of the main fag.Following humanvalidation,a decimal fraction “.*” is added to the main fag, refecting the agreement of the spec-troscopic measurement(zspec),to the photometric redshift(zphot), estimated from the fve-band CFHTLS photometry. Photometric redshifts have been derived using Le Phare (Ilbert et al. 2006; Arnouts& Ilbert2011),acodethatprovidesus,ontopofthe best redshift solution, zphot, with a specifc 68% confdence interval[ˆzph−min, zˆph−max]for eachgalaxy.To quantify the level of agreement between zspec and zphot we also consider theoverall error distribution that canbe constructedby plotting spectroscopic and photometric redshifts against each other. Early in the survey,we adoptedavalueof σz = 0.025 for the standard deviation (68% interval) of the photometric redshifts, slightly smaller than the current more robust estimate using the median absolute de-viationin the VIPERS data(σz = 0.03, Garilli et al. 2014). The decimal fag is defned as follows. 
– We look frst at whetherzspec is included in the 95.4% (2σ) interval defned by the overall statistics of photometric red-shifts, i.e. the interval zphot ± 0.05 × (1 + zphot). In this case, there are two options: 
1) ifˆzph−min < zspec < zˆph−max, i.e. the spectroscopic redshift alsofalls within the (stricter) 68% interval of the individual PDF, this is defned as “full agreement” and a value 0.5 is added to the original (integer) fag; 
2) if not, the two measurements are defned to be only in “marginal agreement”, and a fag 0.4 is added. 
– 
When neither of the previous criteria is satisfed, a value 0.2 is added. 

– 
When no zphot estimate is available, a value 0.1 is added. 


The rationale behind the decimal fag is to improve the confdencein poorly measured spectroscopic estimates.Forexample, confdence in a highly uncertain (fag = 1) spectroscopic red-shift,wouldbe increasedin caseits comparisonto zphot promotes it to fag = 1.5. 
In all VIPERS papers, redshifts characterisedbya fag rang-ing between 2.X and 9.X are referred to as reliable (or se-cure) redshifts and are the only ones normally used in the science analyses. It might sound riskyto consider objects with fag 
9.2 as reliable. As explained above, these correspond to a red-shift measurement based on one single emission line (normally [OII]3727Å),whichdoesnotagreewiththegalaxy photometric redshift estimate.To confm this, we inspected directly the spectraforall1027such casesinthe PDR-1 sample.For171of these the single-line spectroscopic redshift is close to the photometric one, although not satisfying the statistical criteria to be defned in agreement. Thevast majority(∼95%)of these cases present other features in the spectrum that allow us to promote their fag to 2. For the remaining 856, there is no way the observed emission line could be matched to the photometric redshifts, if associated to oneof the other standardgalaxy emission lines. 
5.3. Error on redshift measurements 
For 783galaxiesin the VIPERS PDR-1 samplea repeated, reliable redshift measurement exists. These are objects lying at the border of the quadrants, where two quadrants overlap, and were therefore observed by two independent pointings. In addition, during the re-commissioning of VIMOS after the CCD refurbishment in summer 2010, a few pointings were re-observed to verify the performances with the new set-up(Hammersleyet al. 2010),targeting another 1357galaxies.In total, thisgivesa sam<BR>ple of 1941 galaxies with double observations. 1215 of these yield a reliable redshift (i.e. with a fag ≥2) in both measurements and can be conveniently used to obtain an estimate of the internal rmsvalueof the redshift errorof VIPERSgalaxies. 
The bottom panel of Fig. 8 shows the distribution of the differences between these double measurements. The sign of these differences is clearly arbitrary. These have been computed as z2 − z1, where “1” and “2” are chronologically ordered in terms of observation date. Once normalised to the corresponding redshift expansion factor 1 + z, the overall distribution of these measurements is very well described by a Gaussian with a dispersion of σ2 = 200 kms−1, corresponding to a single
√ 
object1σ error σv = σ2/ 2= 141 km s−1. In terms of redshift, this yields a standard deviation on the redshift measurements of 0.00047(1 + z). If we restrict ourselves to the highest quality spectra (i.e. fags 3 and 4), we are left with 655 double mea-surements; the resulting rest-frame 2-object dispersion changes very little, decreasing to σ2 = 193 kms−1. This indicates that fags2,3and4 are substantiallyequivalentin termsof redshift precision. 
5.4. Confdence level of quality fags 
Repeated observations allow us to quantify in an objective way the statistical meaning of our quality fags, which are by nature subjective;theyare assignedbyindividualsinalarge, geographically dispersed team. Remarkably, the grading system turns out to be quite stable and well-defned as we show hereafter. 
Let us defne two redshifts as “in agreement” when Δz/(1 + z)< 3σz ' 0.0025.We compare the redshiftsof double measurements from the VIPERS sample only, considering the fag assigned to both measurements. Flags3 and4 are considered together, as theyshould not be different in practice in terms 
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Fig. <BR>8. <BR>Distribution of redshift differences between two independent measurements of the same object, obtained from a set of 1215 VIPERS galaxies with quality fag ≥ <BR>2. In the bottom <BR>panel, the darker dots correspond to top-quality redshifts (i.e. fags 3 and 4), which show a dispersion substantially similar to the complete sample (see text). Catastrophicfailures (defned as being discrepantby more than Δz <BR>= <BR>6.6×10−3(1+ <BR>z))haveobviously beenexcluded.Top:distribution of the corresponding differences Δv <BR>= <BR>cΔz/(1 + <BR>z). The best-ftting Gaussian has a dispersion of √ <BR>σ2 = <BR>200 kms−1, corresponding to a single-object rms error σv <BR>= <BR>σ2/ <BR>2= <BR>141 kms−1. In terms of redshift, this translates into a standard deviation of σz <BR>= <BR>0.00047(1 + <BR>z)fora singlegalaxy measurement. of strict redshift reliability.We therefore consider pairs of mea-surements, in the following cases: 1. Both measurements have fag = <BR>3 or 4: out of 655 pairs,5 have discrepant redshift. 2. One measurement has fag = <BR>2and the other3/4: In this case we assume the measurement with fag3/4to be the correct one.Wehave10 fag = <BR>2redshifts that are discrepant, out of 345. 3. Both measurements have fag= <BR>2: 22 out of 148 pairs have discrepant redshift. 4. One measurement has fag = <BR>1 and the other has 2,3 or 4: 121 out of 301 are discrepant. 5. Both measurements have fag = <BR>1:56 outof74 are discrepant. With the reasonable assumption that when two redshifts are in agreement theyare both correct, using these data we can derivea confdence level of the redshift measurements for each fag class, which we report inTable 1.A fnal comment should be added concerning Flag9objects, i.e. those redshifts based ona single emission line (tipically interpreted as [OII]λ3727), in particular when theydisagree with the photometric redshift (Flag 9.2).We do not have sufficient statistics for this class in the sample with repeated observations. Their reliability is discussed in more de<BR>tailinthePDR-1data releasepaper(Garillietal.2014). 6. Surveyselection function The VIPERS angular selection function is the result of the com-bination of several different angular completeness functions. Table 1. Redshift confdence levels corresponding to the VIPERS quality fags, estimated from pairsof measurementsof the samegalaxy. 
Flag class z confdence level 
3+4 99.6% 2 95.1% 1 57.5% 
Two of these are binary masks (i.e. areas are fully used or fully lost). The frst mask is related to defects in the parent photometric sample (mostly areas masked by bright stars) and the other to the specifc footprint of VIMOS and how the different pointings are tailored together to mosaic the VIPERS area. Moreover, within eachofthe four VIMOS quadrantsonlyanaverage40%oftheavailabletargets satisfyingthe selection criteria are actually placed behind a slit and observed, defning what we call the Target Sampling Rate (TSR). Finally, varying observing conditions and technical issues determine a variation from quadrant to quadrant of the actual number of redshifts measured with respecttothe numberoftargetedgalaxies,whatwecallthe Spectroscopic Success Rate (SSR). 
Detailed knowledge of all these contributions is a crucial ingredient for anyquantitative measurement ofgalaxy clustering. In principle, there will also be variations of the TSR and SSR within a single quadrant, owing to the details of the response of slit assignment to small-scale clustering, and to internal dis-tortions that may cause the slits to be slightly misplaced on the sky. Theseeffects are hard to represent simply,since theycannot be viewed purely as a position-dependent probability of obtaining a redshift. This is because the fnite size of the slits means that close pairs of galaxies cannot be sampled, and there will always be some complex structure in the statistics of pair separations owing to the surveyselection. Once the main quadrantbased corrections are made, the only practical way of dealing with these is to use the known statistics of angular clustering in the initial photometric catalogue in order to make a fnal small correctiontothe estimated clustering statistics(delaTorreetal. 2013). 
6.1. Revised CFHTLS photometric mask 
The photometric quality across the CFHTLS images is tracked with a set of masks accounting for imaging artefacts and non-uniformcoverage.Weusethe maskstoexcluderegionsfromthe surveyarea with corrupted source extraction or degraded photometric quality. The masks consist primarily of patches around bright stars(BVega < 17.5) owing to the broad diffraction pat-tern and internal refections in the telescope optics. At the core of a saturated stellar halo there are no reliable detections, leaving a hole in the source catalogue, while in the halo and diffraction spikes spurious sources may appear in the catalogue due to false detections.Wealsoaddtothemaskextendedextragalactic sources that may be fragmented into multiple detections or that may obscure potential VIPERS sources. The masks are stored in DS9 region fle format using the polygon data structure. 
Terapix includedabrightstarmaskaspartoftheT0006data release consisting of star-shaped polygons centred on the stellar halos.We found this mask to be too restrictive for VIPERS; in particular,wefoundthatthe arealostwasexcessive neardiffraction spikes and within stellar halos.We follow the same strat-egyin constructing the VIPERS mask,but instead usea circular 
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Fig. <BR>9. <BR>Visual display of the masks developed for VIPERS, inside a 1 deg2 region of the survey. The new bright-star mask is marked by the magentacirclesand cross patterns,whiletheoriginalmask distributedbyTerapix,basedonthefour-pointstartemplate,isshowningreen;orange polygons are drawn around selected extended sources. The quadrants that make up the VIPERS pointings are plotted in red. In the background is the CFHTLS T0006 χ2 imageofthe feld 020631-050800 producedbyTerapix. Notethe signifcantgainin usableskyobtained withthenew 
VIPERS-specifc mask. 
templatewitha cross pattern.Theangularsizeofthe templateis scaled based upon the magnitude of the star. 
Our starting point for the bright star mask was the USNOB 1.0 catalogue(Monet et al. 2003), from which we selected a sample of stars with BVega < 17.5. Using the full CFHTLS area (130 deg2), we measured the mean source density in the photometric catalogue as a function of distance from a bright star.We used the density profle to calibrate a size–magnitude relation for the stellar halo.We derived the following relations for the star magnitude B and the halo radius R in arcminutes: 
B < 15.19: log10(R)= −2.60 log10(B)+ 2.33 (10) 
B ≥ 15.19: log10(R)= −6.55 log10(B)+ 6.99. (11) For stars brighter thanB = 17we includea cross patterntocover the diffraction spikes.For the brightest ∼200 stars with B < 11, we inspected the χ2 image (see Szalay et al. 1999)and adjusted the masks individually. The USNOBcatalogue includesa num-berofextended sources thatin manycaseshave multiple entries. We cross-checked the catalogue against the 2MASS Extended Source Catalogue to remove duplicates. A zoom into the W1 feld, showing thevarious masks,is displayedin Fig. 9. 
Although signifcant attention was given to constructing a homogeneous imaging surveyinfve bands,a handfulof patches exist within the W1 feld that have degraded photometric quality in one band. These regions were identifed based upon high values of the photometric redshift χ2.We include these regions as rectangular patchesinthe photometric mask, visibleinFig. 9. No such regions were identifed in the W4 feld. 
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6.2. Spectroscopic mask and weights 
Although the general layout of VIMOS is well known, the precise geometry of each quadrant’s observations need to be specifed carefully, in order to perform precise clustering mea-surements with the VIPERS data. Although it happens rarely, a quadrant may be partly vignetted by the guide probe arm, in those cases in which no better located guide star could be found. In addition,the accuratesizeand geometryofeach quadrantwas changed between the pre-and the post-refurbishment data (i.e. from mid-2010 on), due to the dismounting of the instrument andthe technical featuresofthenew CCDs.Wehad thereforeto build our own extra mask of the spectroscopic data, accounting forallthese aspectsatanygivenpointontheskycoveredbythe survey. 
The masks for the W1 and W4 data were constructed from the pre-imaging observations by running an image analysis routine that identifes “good” regions. First, a polygon is defned that traces the edge of the image. The mean and variance of the pixels are computed in small patches at the vertices of the poly-gon. These measurements are compared to the statistics at the centre of the image. The vertices of the polygon are then iteratively moved inward toward the centre until the statistics along the boundary are within an acceptable range. The boundary that results from this algorithm is used as the basis for the feld geometry. The polygon is next simplifed to reduce the vertex count: short segments that are nearly co-linear are replacedbylong segments. The WCS information in the fts header is used to convert from pixel coordinates to skycoordinates. Each mask was then examined by eye. Features due to stars at the edge of an image were removed, wiggly segments were straightened and artefacts due to moon refections were corrected. The red lines in Fig. 9 show the detailed borders of the VIMOS quadrants, describing the spectroscopic mask. 
Before scientifc analyses can be performed on the ob-served data, knowledge of two more selection functions (angu-lar masks) is needed, i.e. the TSR and SSR mentioned earlier. The variation of the TSR as a function of quadrant is shown in Fig. 10, refecting the intrinsic fuctuations in the number den-sityofgalaxiesasa functionof positiononthesky. Thanksto the adopted strategy (i.e. having discarded through the colour selection almost half of the magnitude-limited sample lying at z < 0.5), the average TSR of VIPERS is >40%, a fairly high value that represents one of the specifc important features of VIPERS. This can also be appreciated in Fig. 12 (bottom histogram), where we plot the TSR integrated over the whole sur-vey, as a function of galaxy magnitude. Note how the TSR is substantially independent of the target magnitude. 
Similarly, the SSR corresponding to measuring a reliable redshift (fag = 2, 3, 4, 9) over the VIMOS quadrants is shown in Fig. 11. Here one can appreciate how for the majority of the survey area we have SSR2,3,4,9 > 80%.Afew observations under problematic conditions (either technical or atmospheric) are clearly marked out by the brown and purple rectangles. In Fig. 12 (top histograms) we also plot the SSR integrated over the whole survey as a function of the target magnitude, as de-tailed in the caption. As one would expect, faintest galaxies are harder to measure: at the very limit of the VIPERS survey (22 < iAB < 22.5), a redshift is delivered for ∼90%of thegalaxies; a reliable redshift is obtained for as many as ∼75% of the targetedgalaxies. The SSR shown asa colour scalein Fig. 11 corresponds specifcally to the latter case, integrated over all magnitudes. 
Through the observed dependence on apparent magnitude, one would expect in general a dependence of the SSR on red-shift, SSR(mag(z)). An explicit dependence may in principle also arise, however, due to the varying ability to identify spec-tral lines in regions of higher noise (e.g. where the “forest” of sky lines is stronger, at λ> 8000 Å). In practice, an estimate of the full SSR(mag, z)can be obtained by using photometric redshifts for the unobserved targets. In this approach it is as-sumed that the quadrant-dependence SSR(Q)can be separated 
(i.e. only contributes a scaling factor) from the SSR(mag, z). Such SSR(mag, z)has been used, e.g., in the computation of the luminosity and mass functions(Davidzon et al. 2013;Fritz et al. 2014). 
For some specifc analyses one may have to further correct forangularvariationsoftheTSRandSSRon scales smallerthan thoseofa single quadrant.FortheTSR, thisisthe caseof clustering measurements, to account for the “proximity bias” arising from the combination of the fnite size of slits and spectra and 
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the single-pass strategy, which affects angular correlations be-low 0.05degrees(delaTorreetal. 2013).VariationsoftheSSR on sub-quadrant scales are less likely, but could arise, for example, in case of optical feld distortions that would produce an imperfect centring of the slits on the objects at the corner of the quadrants. 
More discussion on such details is presented in the paper accompanying the PDR-1 catalogue(Garilli et al. 2014). 
7. Results and perspectives 
Experience with the frst half of the VIPERS dataset fully con-frms the expected general performance and science potential of the survey. As shown here, the average quality of the redshifts is as expected, with typical redshift measurement errors that are even better than in previous similar surveys with VIMOS. Figure 13 shows the redshift distribution of the data collected so far in the two felds. The combination of the two felds provides an impressively smooth distribution, averaging over local structure. As discussed earlier,the surveyis complete beyond z = 0.6, witha transition region at0.4< z < 0.6producedby the colour– colour selection.Asubstantial tail ofgalaxies out to z = 1.4is also apparent. This redshift range benefts particularly strongly from the increased sensitivity and lack of substantial fringing with the refurbished VIMOS CCDs, allowinga clearer detection of the [OII]3727 line or the 4000Åbreakbeyond 8000Å. 
The most striking result from this frst signifcant set of VIPERS observations is provided by the new maps of the 3D galaxy distribution in the range0.5 < z < 1.2, which we show in the cone diagramsof Fig. 14.As demonstratedby these plots, VIPERS provides an unpredecented combination of overall size and detailed sampling, yielding a representative picture of the overall galaxy population and large-scale structure when the Universe was about half its current age. A direct comparison of VIPERS with local surveys, in terms of size and redshift, is shown in Fig. 15. Here the VIPERS redshift data are plotted to-gether with those from the SDSS-Main and SDSS-LRG surveys. The fdelity with which structure can be seen in VIPERS (covering linear scales ∼ Gpc) is comparable, at high redshifts, to that of SDSS-Main at z < 0.1, while the lower density of the LRG sample conveys little visual impression of signifcant structure. 
New statistical measurements of clustering are being ob-tained with these results. Moreover, the rich and high-quality set of ancillary photometric data, combined with the distance information,isallowingusto computethekeymetadata(SED,luminosities, stellar masses) for quantifying the connection between galaxy properties and the surrounding structure at these early epochs. An example of the power of correlating galaxy properties with the surrounding large-scale structure is provided by Fig. 16, which represents a zoom into part of the W1 VIPERS volume. Here galaxies have been coloured according to their rest-frame U − B colour,providingin thiswayobviousevidence that the present-day colour–density relation had already been es-tablished at these redshifts. 
The scientifcinvestigationsof the VIPERSTeam using this rich datasethave focused sofar ona seriesof goals, which we briefy list here: 
– 
To measure the growth of structure between z = 1.2 and 0.5,by modelling the anisotropyof clustering(delaTorre et al. 2013). The initial application is to the galaxy popu-lation treated as a whole, but the high sampling and good spectroscopic completeness means that we will be able to exploit the use of multiple populations to reduce statistical and systematic errors in this measurement. 

– 
To measure in detail the clustering of galaxies on small/intermediate scales at 0.5 < z < 1, quantifying its dependence on luminosity and stellar mass(Marulli et al. 2013). The fnal goal here is to describe the relation be-tween baryons and dark matter, measuring the evolution of thegalaxyHOD(delaTorreetal.,in prep.). 

– 
To measure the power spectrum of the galaxy distribution P(k, z)over 0.5 < z1.2 (Rota et al., in prep.), constraining cosmological parameters like the matter density parameter(Bel et al. 2014), and the neutrino mass and number of species(Granett et al. 2012;Xia et al. 2012). 

– 
To measure the luminosity and stellar mass functions to high statistical accuracy at 0.5 < z < 1, in particular at the bright/massive end(Davidzon et al. 2013). 


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Fig. <BR>12. <BR>Target sampling rate (TSR, lower histograms) and spectroscopic success rate (SSR, upper histograms), as a function ofgalaxy magnitudes. TSR and SSR are defned as in the text,but here the TSR is shown for two cases: the solid line corresponds to considering as tar-gets only those objects for which an actual spectrum is detected in the corresponding slit; the dot-dashed line considers instead all placed slits, independently from having an actual spectrum detected (which is what we usually call TSR and is shown quadrant-by-quadrant in Fig. 10). This latter plot shows that slits are placed on targets in a way that is substantially independentofgalaxy magnitudes, witha slight tendency tofavour thefainter objects. The solid histogram shows on one side that about 2% of the targets are not detected, on average, for iAB > 20, with this fraction increasing from 0.5% to 4% when going from iAB = 20 to 22.5. On the other hand, it also shows a difficulty in detecting and extracting the spectra at the bright end(iAB < 19).We understand this as due to the brighter (larger) objects flling completely the short slits adopted, thus making their detection in the 2D spectrum more difficult (see also Garilli et al. 2014).For the SSR (top curves), the two solid histograms give the success rate with respect to the detected targets, but using respectively all measured redshifts (fag≥1, lighter shading) or only the “reliable” redshifts (fag ≥2, medium shading). The short-dashed line gives instead the fag ≥1SSR,but referred to all targeted galaxies. The SSR drops for the very bright objects, due to the reason discussed for the TSR. See Garilli et al. (2014)for more discussion on using these quantities in statistical calculations. 
– 
More generally, to make a full characterisation of the evolutionofgalaxiesover this important rangeof redshifts,in terms of the distributions of other fundamental properties like colours, spectral types and star-formation rates(Fritz et al. 2014). This will also benefts of the high quality of the CFHTLS observations in terms of average PSF to push sim-ple morphological estimations(bulge/disk decomposition) to the higher redshifts explored by VIPERS (Krywult et al., in prep.). 

– 
To measure higher-order clustering statistics at this early epoch, where mass fuctuations are closer to the linear regime, measuring the moments of the galaxy distribution (Cappi et al., in prep.) and the evolution and nonlinearity of galaxy biasing (Di Porto et al., in prep.). 

– 
To construct a large and well defned sample of optically se-lected groupsand clustersatat0.5< z < 1, to investigate the properties of these systems and in particular the evolution of galaxies in different environments (Iovino et al., in prep.). 




(solid red and blue lines, respectively). All measured redshifts (fag =1 and above) have been plotted here. The redshift histogram restricted to only the most reliable redshifts (fag ≥2) does not show signifcant differences. 
– 
To reconstruct the density feld over a large volume and dy-namic range at 0.5 < z < 1, properly accounting for the specifcity of the VIPERS footprint and sampling(Cucciati et al. 2014). 

– 
Using this density feld, to produce an order-of-magnitude improvement in our knowledge of crucial relationships be-tween galaxy properties and their environment, as colours (Cucciati et al., in prep.), and stellar masses (Davidzon et al., in prep.). 

– 
To construct a massive spectroscopic and multi-band photometric database, with automatic spectral classifcations through SED-ftting, Principal Component Analysis (Marchetti et al. 2013)and other techniques, such as super-vised learning algorithm methods(Ma ek et al. 2013). 

– 
To cross-correlate the detailed3D mapsof thegalaxy distribution with the dark-matter maps reconstructed using weak lensing from the CFHTLS high-quality images. 

– 
TomeasurethefaintendoftheAGN luminosity functionand their correlation with large-scale structure, through a dedicated sub-sample. 


This is a substantial list of what should prove to be exciting de-velopments, representing a major advance in our knowledge of the structure in the Universe around redshift unity. But all these applications should beneft from more detailed investigation, and there are many fruitful topics beyond those listed above. We hope, and expect, that VIPERS will follow in the path of the major low-redshift surveys in generating many more important papersfromopenuseofthepublicdata.We therefore encourage readers to start using the PDR-1 data release4,described in detail in its accompanying paper(Garilli et al. 2014).This should serve to increase anticipation for what may be achieved with the fnal VIPERS dataset, which, given the current statistics, is expected to include around 90 000 redshifts. 
4 Available athttp://vipers.inaf.it <BR>
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VIPERS. 
Acknowledgements. We acknowledge the crucial contribution of the ESO staff for the management of service observations. In particular, we are deeply grateful to M. Hilker for his constant help and support to this program. Italian participation to VIPERS has been funded by INAF through PRIN 2008 and 2010 pro-grams. L.G., B.R.G. and J.B. acknowledge support from the European Research Council through the Darklight ERC Advanced Research Grant (# 291521). 
O.L.F. acknowledges support from the European Research Council through the EARLY ERC Advanced Research Grant (# 268107). Polish participants have been supported by the Polish Ministry of Science (grant N203 51 29 38), the Polish-Swiss Astro Project (co-fnancedbyagrant from Switzerland, through the Swiss Contribution to the enlarged European Union), the European Associated Laboratory Astrophysics Poland-France HECOLS and a Japan Society for the Promotion of Science (JSPS) Postdoctoral Fellowship forForeign Researchers (P11802). G.D.L. acknowledges fnancial support from the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement No. 202781.W.J.P. and R.T. acknowledge fnancial support from the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007–2013)/ERC grant agreement No. 202686.W.J.P. is also grateful for support from the UK Science andTechnologyFacilities Council through the grant ST/I001204/1. E.B.,F.M. and L.M. acknowledge support from grants ASI-INAF I/023/12/0 and PRIN MIUR 2010–2011. Y.M. acknowledges support from CNRS/INSU (Institut National des Sciences de l’Univers) and the Programme National Galaxies et Cosmologie (PNCG). C.M. is grateful for support from specifc project funding of the Institut Universitaire de France and the LABEX OCEVU. 
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L. Guzzo et al.: The VIMOS Public Extragalactic Redshift Survey(VIPERS) 

0.9 and 1.2, or >1.2. Also in this case the size of the dots has been set proportionally to the B-band luminosity of the correspondinggalaxy. The plot shows clearly that the colour–density relation forgalaxies is already in place at these redshifts(Cucciati et al. 2006), with red early-type galaxies tracing the backbone of structure and blue/greenstar-formingobjectsfllingthe more peripherallower-densityregions.Thispicturegives anexampleofthe potentialof VIPERSfor studyingthe clusteringofgalaxiesasa functionofgalaxy properties,over scales rangingfromlessthan 
a Mpc to well above 100 Mpc. 
Appendix A: VIPERS star–galaxy separation 
The star/galaxy classifcation scheme developed to construct the VIPERS target sample benefts from the high-quality CFHTLS photometric data combined with the available spectroscopic information for a signifcant number of objects in both W1 and W4 provided by the VVDS Deep and Wide surveys. The CFHTLS photometric data are particularly suited for this opera-tion. Having been designed for weak-lensing studies, theybeneft of sub-arcsec seeing over most of the survey which makes identifcation of point sources much easier compared to other surveys. This is a signifcant asset of VIPERS and allows us to perform an accurate star/galaxy selection and in turn make efficient use of telescope time. This is particularly important as in a purely magnitude limited sample of objects at iAB < 22.5the fraction of stars can be larger than 30% (as it is the case in the W4 feld). 
A key ingredient in identifying the optimal selection cri-teria for star–galaxy separation is provided by the two large and complete pre-existing spectroscopic samples in VIPERS felds, i.e. VVDS-Deep (Le Fèvre et al. 2005) and VVDS-Wide(Garilli et al. 2008). VVDS-Deep provides redshifts for more than 10000galaxies,AGNs and stars to iAB = 24, over a ∼0.5 deg2 area in W1. The F22 feld of VVDS-Wide, in-stead, includes spectra over 4 deg2 for 11 200 galaxies and ∼7000 stars to i = 22.5, in W4. These two VVDS samples are purely magnitude-limited surveys. They represent therefore an ideal control sample to test the completeness and contamination of anyselection criterion. Here we use only the most secure unambiguous spectra and restrict the Deep andWide VVDS cataloguestoonlyfag3and4objects (defnedina scheme analogous to that described in Sect. 5.2). 
A.1. Methods and tests 
The method adopted to classify stars andgalaxies for VIPERS combines knowledge of the object size, provided by the half-light radius rh (i.e. the radius containing half of the object’s fux), with that of its reconstructed spectral energy distribution (SED), obtained through template ftting of the available fve-band photometry. 
The excellent image quality of the CFHTLS data suggests that at the VIPERS magnitudes the object size rh should pro-videtheprimewayto distinguish starsfromgalaxies.Figure A.1 plots the magnitude and the size of a complete set of spectroscopically identifed stars and galaxies from the VVDS-Wide survey(Garillietal.2008)whichoverlapstile#5ofthe VIPERS W4 area. The sharply defned locus occupied by stars (blue asterisks), defnes the typical size of a point-like source in this tile which depends on the tile seeing (note that the few red points appearing over the stellar locus for i < 21 correspond toAGN). In order to characterise the intrinsic point spread function (PSF) of each tile, we select objects with 17.5< i < 21 where stars are dominant and ft a Gaussian to the rh distribution. The statistical distribution of stellar sizes within a specifc tile in this way can be described in terms of its mean(µrh)and standard deviation (σrh). Looking at Fig. A.1, it is natural to defne as stars objects with rh <µrh + 3σrh.EvenexcludingAGN interlopers,however, one sees that for magnitudes fainter than i ' 21 a number of smallgalaxiesexist whichwouldbe mistaken as starsby purely geometrical criteria. 
To recovergalaxies at thefaintest limit and increase com-pleteness of the galaxy sample we add therefore the type in-formation provided by the object SED. This is obtained by ftting the fve-band CFHTLS photometry with the Le Phare 
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A&A 566, A108 (2014) 

ject’s fux, rh, with the i magnitude. This is done here for a complete set of spectroscopically identifed stars andgalaxies from the VVDS-Wide survey(Garilli et al. 2008). All objects belong to tile #5, in the overlap-ping region with the VIPERS W4 area, and are therefore characterised by a uniform seeing (see text). Stars are plotted as blue asterisks and galaxies as red points. The locus of point-like sources is well defned, suggestingaclear strategyfor star–galaxy separationas discussedinthe text. The few red points lying within the stellar locus at bright magnitudes(iAB < 21) correspond to (point-like)AGNs. 
photometric redshift code. Among a library of SEDs, the bestftting χ2 is identifed for bothgalaxy(χ2)and stellar(χ2) 
galstar 
templates.An objectis then classifed asagalaxy (star)if χ2 is 
gal 
smaller (larger) than χ2 . The corresponding limitation of this 
star
technique is that with the available optical(u g riz)bands, there isadegeneracyin colourof some starsandgalaxies whichwould result in signifcant stellar contamination if only this method is used. This is shown by the plots of Fig. A.2. For this reason the fnal VIPERS criteria have been defned as a combination of these two methods. 
To quantify the performances of our different selection cri-teria, we frst defne incompleteness and contamination. Let us defne Nestthenumberofobjects classifedasgalaxiesbyagiven method; this contains both realgalaxies Nest−true and stars mis-classifed asgalaxies Nest−fake,such thatNest = Nest−true +Nest−fake. Let us also call Ntruethe total numberofgalaxiesinthe sample. Using our VVDS control samples we know all these contributions and can thus estimate the intrinsic theoretical incompleteness of a selection method as 
(Ntrue − Nest−true) 
Inc = · (A.1) Ntrue 
Similarly, the theoretical sample contamination is 
Nest−fake 
Cnt = · (A.2) Ntrue 
Clearly, in real observations we only know Nest = Nest−true + Nest−fake, and we can only defne incompleteness and contamination with respect to the recovered sample ofgalaxies.For test-ing these methods with the VVDS data, however, here we have 
Fig. <BR>A.2. <BR>Distribution of log(χ2)− log(χ2 )for spectroscopically con-
star gal
frmed stars (dashed histograms) and galaxies (solid histograms) for the VVDS-Wide spectroscopic sample in W4. The sample is split into a bright and faint sample, corresponding to the split used to classify VIPERS galaxies. Ideally, one would expect that all galaxies have 
χ2 − χ2 
gal < 0, while stars are confned to positive values. However, as can be seen, tails of both populations overlap each other. Top:no se-lection is applied on the half-fux radius rh. Middle: only objects with rh ≥ µrh + 3σrh are considered (i.e. “geometric”galaxies). Bottom:only objects with rh <µrh + 3σrh are considered (i.e. “geometric” stars). 
star 
preferred to work with the intrinsic expected quantities defned above. 
After signifcant experimentation, the VIPERS stars-galaxy separation has been defned through the following combination of the two methods discussed earlier: 
1. 
At i < 21, stars are defned to be simply objects with rh < µrh + 3σrh. Galaxies are the complementary class. 

2. 
At i ≥ 21, stars are defned as having rh <µrh + 3σrh, but requiring in addition that log(χ2) < log(χ2)+ 1. In 


stargal
this way, small-sized faint galaxies (i.e. objects for which 
log(χ2)≥ log(χ2)+ 1ORrh ≥ µrh + 3σrh)are added to the 
stargal
sample thus increasing its completeness. 
Applying this combination to the VVDS-Wide and VVDS-Deep test samples, we obtain the completeness Inc and contamination Cnt levels that are summarised in Table A.1. Within the limi<BR>tations of the sample sizes, the fgures in this table should rep-resent a good indication of the estimated percentages expected in actual VIPERS data. The contamination level is the only one that canbe checked directly using the actual observed data, to verify these predictions on a much largers sample. Considering thePDR-1data,the outcomeisextremely encouraging.Together with the 53608 confrmed galaxy spectra, the data composing 
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actual VIPERS target (Sect. 4). Appendix B: i-band flter transformation between T0005 and T0006 L. Guzzo et al.: The VIMOS Public Extragalactic Redshift Survey(VIPERS) 
Table A.1. Incompleteness and contamination of the VIPERSgalaxy sample expected from the star–galaxy separation process, estimated by applying the fnal criteria discussed in the text to the VVDS Deep and Wide complete catalogues toiAB = 22.5. 
Field Inc Cnt 
W1 (VVDS-Deep) 2.07% (2.13)% 0.87% (0.27%) W4 (VVDS-Wide) 0.96% (0.64%) 6.59% (8.24%) 
Notes. The values in parentheses give the values corresponding to galaxies colour-selected to be atz > 0.5, i.e. that would be part of the 
Fig. B.1. Colour transformation between the i-band magnitudes of ob-jects in tile 022929–060400, as measured in the CFHTLS T0006 and T0005 catalogues using the original i∗ flter and its replacement (called y or i2, see text). 
the PDR-1 catalogue have yelded also a set of 1750 stars that had been erroneously classifed asgalaxies and thus observed. This is what we called Nest−fake in our scheme.To transform this precisely into a contamination Cnt, we should know the incompleteness Inc astoknowthetrueexpected numberofgalaxiesin the sample. This cannot be obviously obtained from the observations. However, we can assume that the mean incompleteness is close to the value estimated from the VVDS samples and see whether the contamination agrees with the original expectation. Since the two samples from W1 and W4 composing the PDR-1 data set are very similar in number, the total incompleteness ex-pected if we use the percentages estimated for the two felds in TableA.1 is given by 
(1− 0.0213) + (1− 0.0064) 
Inctot ' 1− = 1.39%. (A.3) 
2 
With this incompleteness, the average contamination in the cur-rent PDR-1 sample is 
1750 
Cnttot == 3.22%, (A.4) 
53608(1 + 0.0139) 
which on average is better than the mean value expected from the third column ofTable A.1. If we do the same separately for the two felds W1 and W4, we obtain a contamination of 1.5% 
Table C.1. Cross-reference between the VIPERS numbering scheme and the corresponding CFHTLS tiles in the W1 feld. 
W1 VIPERSTile# CFHTLS name 
01 CFHTLS_W_ugriz_020241-060400_T0005 02 CFHTLS_W_ugriz_020631-060400_T0005 03 CFHTLS_W_ugriz_021021-060400_T0005 04 CFHTLS_W_ugriz_021410-060400_T0005 05 CFHTLS_W_ugriz_021800-060400_T0005 06 CFHTLS_W_ugriz_022150-060400_T0005 07 CFHTLS_W_ugriz_022539-060400_T0005 
08  CFHTLS_W_ugriz_022929-060400_T0005  
09  CFHTLS_W_ugriz_023319-060400_T0005  
10  CFHTLS_W_ugriz_020241-050800_T0005  
11  CFHTLS_W_ugriz_020631-050800_T0005  
12  CFHTLS_W_ugriz_021021-050800_T0005  
13  CFHTLS_W_ugriz_021410-050800_T0005  
14  CFHTLS_W_ugriz_021800-050800_T0005  
15  CFHTLS_W_ugriz_022150-050800_T0005  
16  CFHTLS_W_ugriz_022539-050800_T0005  
17  CFHTLS_W_ugriz_022929-050800_T0005  
18  CFHTLS_W_ugriz_023319-050800_T0005  
19  CFHTLS_W_ugriz_020241-041200_T0005  
20  CFHTLS_W_ugriz_020631-041200_T0005  
21  CFHTLS_W_ugriz_021021-041200_T0005  
22  CFHTLS_W_ugriz_021410-041200_T0005  
23  CFHTLS_W_ugriz_021800-041200_T0005  
24  CFHTLS_W_ugriz_022150-041200_T0005  
25  CFHTLS_W_ugriz_022539-041200_T0005  
26  CFHTLS_W_ugriz_022929-041200_T0005  
27  CFHTLS_W_ugriz_023319-041200_T0005  

for W1 and 4.9% for W4, i.e. slightly higher than predicted for W1,but signifcantly smaller for W4. 
As mentionedabove,afew observationsfromtheT0006 release that were needed for VIPERS to fll some missing “holes” in the original catalogue wereinfact obtained withadifferent i-band flter with respect to the rest of T0005. The reason for this change was that the original i-band flter at CFHT(i.MP9701)broke in 2006 and was replaced. The new flter, i.MP9702, is called y in TERAPIX documentation and sometimes also referred to as i2. For the small number of objects in the VIPERS areas for which only the T0006 y-band measurement was available we derived a transformation using objects from the regions where both mag-nitudes are available. We considered one tile from the T0005 catalog, CFHTLS_W_ugriz_022929-060400_T0005.cat, and the corresponding T006 catalogue CFHTLS_W_ugryz_022929060400_T0006.catmask. These two lists were matched assuming that the T0005 data was based entirely on observations with the i flter, and that the T0006 data was based entirely on observations with the y flter.For bright and well measured objects 
(18.0 < i < 21.0), we found a mean offset Δi = iT05 − yT06 = −0.052 ± 0.042 mag, and a good correlation between this offset and the observed(r − z)colour, as shown in Fig.B.1, such that Δi = −0.008 − 0.050 ∗ (r − z). Here the(r − z)colour term ac-counts for the different response curve of the two flters.With this correction, all i-band magnitudes in the VIPERS catalogue should be considered as homogeneous. 
A108, page 19 of 21 A&A 566, A108 (2014) 

Table C.2. Cross-reference between the VIPERS numbering scheme and the corresponding CFHTLS tiles in the W4 feld. 
W4 VIPERSTile# CFHTLS name 
01 CFHTLS_W_ugriz_220154+011900_T0005 02 CFHTLS_W_ugriz_220542+011900_T0005 03 CFHTLS_W_ugriz_220930+011900_T0005 04 CFHTLS_W_ugriz_221318+011900_T0005 05 CFHTLS_W_ugriz_221706+011900_T0005 06 CFHTLS_W_ugriz_220154+021500_T0005 07 CFHTLS_W_ugriz_222054+011900_T0005 08 CFHTLS_W_ugriz_220542+021500_T0005 09 CFHTLS_W_ugriz_220930+021500_T0005 10 CFHTLS_W_ugriz_221318+021500_T0005 11 CFHTLS_W_ugriz_221706+021500_T0005 
Appendix C: CFHTLS-VIPERS tiles cross-numbering 
Tables C.1 and C.2 give the cross-reference between the CFHTLS tile names and the corresponding VIPERS internal numbering systems used throughout the surveyselection process and in this paper. 
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1 INAF – Osservatorio Astronomico di Brera, via E. Bianchi 46, 23807 Merate/via Brera 28, 20122 Milano, Italy e-mail: luigi.guzzo@brera.inaf.it 
2 Dipartimento di Fisica, Università di Milano-Bicocca, Piazza della Scienza 3, 20126 Milano, Italy 3 INAF – Istituto di Astrofsica Spaziale e Fisica Cosmica (IASF) Milano, via Bassini 15, 20133 Milano, Italy 4 Aix -Marseille Université, CNRS, LAM (Laboratoire d’Astrophysique de Marseille) UMR 7326, 13388 Marseille, France 5 INAF – Osservatorio Astrofsico di Torino, 10025 Pino Torinese, Italy 6 Canada-France-Hawaii Telescope, 65–1238 Mamalahoa Highway, Kamuela HI 96743, USA 7 Aix-Marseille Université, CNRS, CPT (Centre de Physique Théorique) UMR 7332, 13288 Marseille, France 8 INAF – Osservatorio Astronomico di Bologna, via Ranzani 1, 40127, Bologna, Italy 9 Dipartimento di Matematica e Fisica, Università degli Studi Roma Tre,via dellaVascaNavale84, 00146Roma,Italy 
10 Institute of Cosmology and Gravitation, Dennis Sciama Building, University of Portsmouth, Burnaby Road, Portsmouth PO1 3FX, UK 
11 Institute of Astronomy and Astrophysics, Academia Sinica, PO Box 23-141, 10617Taipei,Taiwan 
12 INAF – Osservatorio AstronomicodiTrieste,viaG.B.Tiepolo11, 34143Trieste, Italy 
13 SUPA, Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ, UK 
14 InstituteofPhysics,JanKochanowskiUniversity,ul. Swietokrzyska 15, 25-406 Kielce, Poland 
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L. Guzzo et al.: The VIMOS Public Extragalactic Redshift Survey(VIPERS) 
15 Department of Particle and Astrophysical Science, Nagoya University, Furo-cho, Chikusa-ku, 464-8602 Nagoya, Japan 
16 Dipartimento di Fisica e Astronomia – Università di Bologna, viale Berti Pichat6/2, 40127 Bologna, Italy 
17 INFN, Sezione di Bologna, viale Berti Pichat6/2, 40127 Bologna, Italy 
18 Institut d’Astrophysique de Paris, UMR7095 CNRS, Université Pierreet Marie Curie,98bis boulevard Arago, 75014Paris, France 
19 Max-Planck-Institut f Extraterrestrische Physik, 84571 Garching 
b. Mchen, Germany 20 Astronomical Observatory of the Jagiellonian University, Orla 171, 30-001 Cracow, Poland 21 National Centre for Nuclear Research, ul. Hoza 69, 00-681 Warszawa, Poland 
22 Universitätssternwarte Mchen, Ludwig-Maximillians Universität, Scheinerstr. 1, 81679 Mchen, Germany 
23 INAF – Istituto di Astrofsica Spaziale e Fisica Cosmica Bologna, via Gobetti 101, 40129 Bologna, Italy 
24 INAF – Istituto di Radioastronomia, via Gobetti 101, 40129 Bologna, Italy 
25 Università degli Studi di Milano, via G. Celoria 16, 20130 Milano, Italy 
26 INFN, Sezione di Roma Tre, via della Vasca Navale 84, 00146 Roma, Italy 
27 INAF – Osservatorio Astronomico di Roma, via Frascati 33, 00040 Monte Porzio Catone, Italy 
28 Laboratoire Lagrange, UMR 7293, Université de Nice Sophia-Antipolis, CNRS, Observatoire de la Ce d’Azur, 06300 Nice, France 
29 Astronomical Observatory of the University of Geneva, ch. d’Ecogia 16, 1290Versoix, Switzerland 
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