@misc{10481/84741,
year = {2023},
month = {7},
url = {https://hdl.handle.net/10481/84741},
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.},
organization = {CERN},
organization = {MICINN, Spain},
organization = {COST},
organization = {ERC},
organization = {ERDF},
organization = {European Union},
organization = {La Caixa Banking
Foundation},
organization = {H2020 Marie Skłodowska-Curie Actions
 	MSCA},
organization = {European Research Council
 ERC},
organization = {Generalitat de Catalunya},
organization = {Agencia Nacional de Promoción Científica y Tecnológica
	ANPCyT},
organization = {Horizon 2020},
organization = {Agencia Nacional de Investigación y Desarrollo
 	ANID},
organization = {Generalitat Valenciana, Spain},
organization = {PIC (Spain)},
publisher = {Springer Nature},
title = {ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset},
doi = {10.1140/epjc/s10052-023-11699-1},
author = {Aad, Georges and Aguilar Saavedra, Juan Antonio and Rodríguez Chala, Mikael and Atlas Collaboration},
}