ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset
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Show full item recordEditorial
Springer Nature
Date
2023-07-31Referencia bibliográfica
Aad, G., Abbott, B., Abbott, D.C. et al. ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset. Eur. Phys. J. C 83, 681 (2023). [https://doi.org/10.1140/epjc/s10052-023-11699-1]
Sponsorship
CERN; MICINN, Spain; COST; ERC; ERDF; European Union; La Caixa Banking Foundation; H2020 Marie Skłodowska-Curie Actions MSCA; European Research Council ERC; Generalitat de Catalunya; Agencia Nacional de Promoción Científica y Tecnológica ANPCyT; Horizon 2020; Agencia Nacional de Investigación y Desarrollo ANID; Generalitat Valenciana, Spain; PIC (Spain)Abstract
The flavour-tagging algorithms developed by the ATLAS Collaboration and used to analyse its dataset of 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 tt¯ events; similarly, at a c-jet identification efficiency of 30%, a light-jet (b-jet) rejection factor of 70 (9) is obtained.