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Współautorami artykułu są członkowie ATLAS Collaboration w liczbie 2303
pl
dc.date.accessioned
2015-07-07T11:31:34Z
dc.date.available
2015-07-07T11:31:34Z
dc.date.issued
2014
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dc.identifier.uri
http://ruj.uj.edu.pl/xmlui/handle/item/11963
dc.language
eng
pl
dc.rights
Dodaję tylko opis bibliograficzny
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dc.rights.uri
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dc.title
A neural network clustering algorithm for the ATLAS silicon pixel detector
pl
dc.type
JournalArticle
pl
dc.abstract.en
A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. This technique reduces the number of clusters shared between tracks in highly energetic jets by up to a factor of three. It also provides more precise position and error estimates of the clusters in both the transverse and longitudinal impact parameter resolution.