ATLAS flavour-tagging algorithms for the LHC Run 2 $pp$ collision dataset
Abstract
The flavour-tagging algorithms developed by the ATLAS Collaboration and used to analyse its dataset of TeV 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% -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 -jet identification efficiency of 30%, a light-jet (-jet) rejection factor of 70 (9) is obtained.
Cite
@article{arxiv.2211.16345,
title = {ATLAS flavour-tagging algorithms for the LHC Run 2 $pp$ collision dataset},
author = {ATLAS Collaboration},
journal= {arXiv preprint arXiv:2211.16345},
year = {2023}
}
Comments
52 pages in total, author list starting page 35, 19 figures, 2 tables, published in EPJC. All figures including auxiliary figures are available at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/FTAG-2019-07/