A neural network clustering algorithm for the ATLAS silicon pixel detector

G Aad; B Abbott; J Abdallah; SA Khalek; O Abdinov; R Aben; B Abi; M Abolins; OS AbouZeid; H Abramowicz; H Abreu; R Abreu; Y Abulaiti; BS Acharya; L Adamczyka; DL Adams; J Adelman; S Adomeit; T Adye; T Agatonovic-Jovin; JA Aguilar-Saavedra; M Agustoni; SP Ahlen; F Ahmadov; G Aielli; H Akerstedt; TPA Åkesson; G Akimoto; AV Akimov; GL Alberghi; J Albert; S Albrand; MJA Verzini; M Aleksa; IN Aleksandrov; C Alexa; G Alexander; G Alexandre; T Alexopoulos; M Alhroob; G Alimonti; L Alio; J Alison; BMM Allbrooke; LJ Allison; PP Allport; J Almond; A Aloisio; A Alonso; F Alonso; C Alpigiani; A Altheimer; BA Gonzalez; MG Alviggi; K Amako; YA Coutinho; C Amelung; D Amidei; SP Amor Dos Santos; A Amorim; S Amoroso; N Amram; G Amundsen; C Anastopoulos; LS Ancu; N Andari; T Andeen; CF Anders; G Anders; KJ Anderson; A Andreazza; V Andrei; XS Anduaga; S Angelidakis; I Angelozzi; P Anger; A Angerami; F Anghinolfi; AV Anisenkov; N Anjos; A Annovi; A Antonaki; M Antonelli; A Antonov; J Antos; F Anulli; M Aoki; LA Bella; R Apolle; G Arabidze; I Aracena; Y Arai; JP Araquea; ATH Arce; JF Arguin; S Argyropoulos; M Arik; AJ Armbruster; O Arnaez; V Arnal

Journal article

A neural network clustering algorithm for the ATLAS silicon pixel detector

G Aad, B Abbott, J Abdallah, SA Khalek, O Abdinov, R Aben, B Abi, M Abolins, OS AbouZeid, H Abramowicz, H Abreu, R Abreu, Y Abulaiti, BS Acharya, L Adamczyka, DL Adams, J Adelman, S Adomeit, T Adye, T Agatonovic-Jovin Show all

Journal of Instrumentation | Published : 2014

DOI: 10.1088/1748-0221/9/09/P09009

Abstract

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. T..

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University of Melbourne Researchers

Federico Scutti Author

Elisabetta Barberio Author

Matteo Volpi Author

G. N. Taylor Author

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Grants

Awarded by Austrian Science Fund

Funding Acknowledgements

We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWF and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF, DNSRC and Lundbeck Foundation, Denmark; EPLANET, ERC and NSRF, European Union; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, DFG, HGF, MPG and AvH Foundation, Germany; GSRT and NSRF, Greece; ISF, MINERVA, GIF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; BRF and RCN, Norway; MNiSW and NCN, Poland; GRICES and FCT, Portugal; MNE/IFA, Romania; MES of Russia and ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SER, SNSF and Cantons of Bern and Geneva, Switzerland; NSC, Taiwan; TAEK, Turkey; STFC, the Royal Society and Leverhulme Trust, United Kingdom; DOE and NSF, United States of America.