The flavour-tagging algorithms developed by the ATLAS Collaboration and used to analyse its dataset of 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 events; similarly, at a c-jet identification efficiency of 30%, a light-jet (b-jet) rejection factor of 70 (9) is obtained.

ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset / Betti, Alessandra; DE SALVO, Alessandro; Di Domenico, Antonio.; Dionisi, Carlo.; Bini, Cesare; Luci, Claudio; POMPA PACCHI, Elena; Morodei, Federico; Lacava, Francesco; SAFAI TEHRANI, Francesco; Artoni, Giacomo; Santi, Lorenzo; Padovano, Giovanni; Martinelli, Luca; Maiani, Luciano; Bauce, Matteo; Gauzzi, Paolo; Gentile, Simonetta; Giagu, Stefano; Ippolito, Valerio. - In: THE EUROPEAN PHYSICAL JOURNAL. C, PARTICLES AND FIELDS. - ISSN 1434-6044. - (2022). [10.1140/epjc/s10052-023-11699-1]

ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset

Betti Alessandra;De Salvo Alessandro;Di Domenico Antonio.;Dionisi Carlo.;Bini Cesare;Luci Claudio;Pompa Pacchi Elena;Morodei Federico;Lacava Francesco;Safai Tehrani Francesco;Artoni Giacomo;Santi Lorenzo;Padovano Giovanni;Martinelli Luca;Maiani Luciano;Bauce Matteo;Gauzzi Paolo;Gentile Simonetta;Giagu Stefano;Ippolito Valerio
2022

Abstract

The flavour-tagging algorithms developed by the ATLAS Collaboration and used to analyse its dataset of 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 events; similarly, at a c-jet identification efficiency of 30%, a light-jet (b-jet) rejection factor of 70 (9) is obtained.
2022
particle physics
01 Pubblicazione su rivista::01a Articolo in rivista
ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset / Betti, Alessandra; DE SALVO, Alessandro; Di Domenico, Antonio.; Dionisi, Carlo.; Bini, Cesare; Luci, Claudio; POMPA PACCHI, Elena; Morodei, Federico; Lacava, Francesco; SAFAI TEHRANI, Francesco; Artoni, Giacomo; Santi, Lorenzo; Padovano, Giovanni; Martinelli, Luca; Maiani, Luciano; Bauce, Matteo; Gauzzi, Paolo; Gentile, Simonetta; Giagu, Stefano; Ippolito, Valerio. - In: THE EUROPEAN PHYSICAL JOURNAL. C, PARTICLES AND FIELDS. - ISSN 1434-6044. - (2022). [10.1140/epjc/s10052-023-11699-1]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1694140
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