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

Summary, in English

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. © 2023, The Author(s).

• Author: G. Aad; T.P.A. Åkesson; E.E. Corrigan; C. Doglioni; P.A. Ekman; J. Geisen; V. Hedberg; H. Herde; G. Jarlskog; B. Konya; E. Lytken; J.U. Mjörnmark; R. Poettgen; N.D. Simpson; E. Skorda; O. Smirnova; L. Zwalinski
• Department/s: Particle and nuclear physics; eSSENCE: The e-Science Collaboration
• Publishing year: 2023
• Language: English
• Publication/Series: European Physical Journal C
• Volume: 83
• Issue: 7
• Links: http://dx.doi.org/10.1140/epjc/s10052-023-11699-1
• Document type: Journal article
• Publisher: Springer
• Topic: Subatomic Physics
• Status: Published
• ISBN/ISSN/Other: ISSN: 1434-6044