ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset
- NázevTitle
- ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision datasetATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset
- Druh výsledkuResult type
- Článek v časopiseJournal article
- AutořiAuthors
- G. Aad, B. Abbott, K. Abeling, N. J. Abicht, B. Ali, K. Augsten, B. Bergmann, T. Billoud, H. Day-Hall, M. Havránek, Z. Hubáček, P. Jačka, S. Mondal, M. Myška, L. Novotný, V. Petousis, R. Polifka, S. Pospíšil, K. Smolek, A. Sopczak, V. Vacek, P. Vokáč, O. Zaplatílek
- Klíčová slovaKeywords
- atlas, flavor tagging
- ExperimentCollaboration
- ATLAS
- DOIDOI
- 10.1140/epjc/s10052-023-11699-1
- Časopis / citaceJournal / citation
- The European Physical Journal C 83(7), 681 (2023) · ISSN 1434-6044
- RokYear
- 2023
- JazykLanguage
- eng
- ZáznamyRecords
- ProjektProject
- Centrum pokročilých aplikovaných přírodních vědCenter for advanced applied sciences; CERN-CZ III - Výzkumná infrastruktura pro experimenty v CERN - LM2023040 (2023–2026)CERN-CZ III - Výzkumná infrastruktura pro experimenty v CERN - LM2023040 (2023–2026)
- CitovánoCited by
- 471 (INSPIRE-HEP)
- Citace ke staženíDownload citation
- TXT · BibTeX
AbstraktAbstract
The flavour-tagging algorithms developed by the AvTLAS Collaboration and used to analyse its dataset of root s = 13 TeV pp collisions from Run 2 of the Large Hadron Collider are presented. These new tagging algorithms are based on recurrent and deep neural networks, and their performance is evaluated in simulated collision events. These developments yield considerable improvements over previous jet-flavour identification strategies. At the 77% b-jet identification efficiency operating point, light-jet (charm-jet) rejection factors of 170 (5) are achieved in a sample of simulated Standard Model t (t) over bar events; similarly, at a c-jet identification efficiency of 30%, a light-jet (b-jet) rejection factor of 70 (9) is obtained.
The flavour-tagging algorithms developed by the AvTLAS Collaboration and used to analyse its dataset of root s = 13 TeV pp collisions from Run 2 of the Large Hadron Collider are presented. These new tagging algorithms are based on recurrent and deep neural networks, and their performance is evaluated in simulated collision events. These developments yield considerable improvements over previous jet-flavour identification strategies. At the 77% b-jet identification efficiency operating point, light-jet (charm-jet) rejection factors of 170 (5) are achieved in a sample of simulated Standard Model t (t) over bar events; similarly, at a c-jet identification efficiency of 30%, a light-jet (b-jet) rejection factor of 70 (9) is obtained.