Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/32557

TitleA neural network clustering algorithm for the ATLAS silicon pixel detector
Author(s)Onofre, A.
Castro, Nuno Filipe Silva Fernandes
ATLAS Collaboration
KeywordsParticle tracking detectors
Particle tracking detectors (Solid-state detectors)
Issue date2014
PublisherIOP Publishing
JournalJournal of Instrumentation
CitationAad, G., Abbott, B., Abdallah, J., Khalek, S. A., Abdinov, O., Aben, R., . . . Collaboration, A. (2014). A neural network clustering algorithm for the ATLAS silicon pixel detector. Journal of Instrumentation, 9. doi: 10.1088/1748-0221/9/09/p09009
Abstract(s)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.
TypeArticle
URIhttp://hdl.handle.net/1822/32557
DOI10.1088/1748-0221/9/09/p09009
ISSN1748-0221
Publisher versionhttp://iopscience.iop.org/1748-0221/9/09/P09009/
Peer-Reviewedyes
AccessOpen access
Appears in Collections:LIP - Artigos/papers

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