Else Lytken
Professor
ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset
Author
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).
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
Document type
Journal article
Publisher
Springer
Topic
- Subatomic Physics
Status
Published
ISBN/ISSN/Other
- ISSN: 1434-6044