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.
Science, Faculty of
TRIUMF
Non UBC
Physics and Astronomy, Department of
Reviewed
Faculty
Researcher
