A neural network clustering algorithm for the ATLAS silicon pixel detector
- NázevTitle
- A neural network clustering algorithm for the ATLAS silicon pixel detectorA neural network clustering algorithm for the ATLAS silicon pixel detector
- Druh výsledkuResult type
- Článek v časopiseJournal article
- AutořiAuthors
- G. Aad, B. Abbott, J. Abdallah, S. Abdel Khalek, O. Abdinov, K. Augsten, P. Gallus, J. Günther, J. Jakůbek, Z. Kohout, V. Král, M. Myška, S. Pospíšil, F. Seifert, V. Šimák, T. Slavíček, K. Smolek, M. Solar, J. Šolc, A. Sopczak, B. Sopko, V. Sopko, D. Tureček, V. Vacek, M. Šuta, P. Vokáč, Z. Vykydal, M. Zeman, M. Suk
- Klíčová slovaKeywords
- Particle tracking detector, Particle tracking detectors (Solid-state detectors)
- ExperimentCollaboration
- ATLAS
- DOIDOI
- 10.1088/1748-0221/9/09/P09009
- Časopis / citaceJournal / citation
- Journal of Instrumentation 9(09), P09009-P09009 (2014) · ISSN 1748-0221
- RokYear
- 2014
- JazykLanguage
- eng
- ZáznamyRecords
- ProjektProject
- Mezinárodní experiment ATLAS-CERNInternational experiment ATLAS-CERN; Podpora zkvalitnění týmů výzkumu a vývoje a rozvoj intersektorální mobility na ČVUT v PrazeSupport of inter-sectoral mobility and quality enhancement of research teams at Czech Technical University in Prague
- CitovánoCited by
- 227 (INSPIRE-HEP)
- Plný text (open access)Full text (open access)
- https://iopscience.iop.org/article/10.1088/1748-0221/9/09/P09009/pdf
- Citace ke staženíDownload citation
- TXT · BibTeX
AbstraktAbstract
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.
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.