Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/10667
Title: ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset
Authors: Aad, G.
Abbott, B.
Abeling, K.
Abicht, N. J.
Abidi, S. H.
Aboulhorma, A.
Abramowicz, H.
Issue Date: 2023
Publisher: Springer
Abstract: 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.
URI: https://doi.org/10.1140/epjc/s10052-023-11699-1
https://hdl.handle.net/20.500.11851/10667
ISSN: 1434-6044
1434-6052
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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