Speaker
Marie Lanfermann
(Universite de Geneve (CH))
Description
A novel higher-level flavour tagging algorithm called DL1 has been developed using a neural network at the ATLAS experiment at the CERN Large Hadron Collider. We have investigated the potential of Deep Learning in flavour tagging using higher-level inputs from lower-level physics-motivated taggers. The DL1 studies presented show that the obtained neural network improves discrimination of b-jets against both light-flavoured-jets and c-jets, and also provides a novel c-tagging possibility, which also makes it a highly flexible tagger. The DL1 tagger is described and a detailed set of performance plots presented, obtained from simulated ttbar events at $\sqrt{s}$=13 TeV and the Run-2 data taking conditions where this tagger will be applied.
Primary author
Marie Lanfermann
(Universite de Geneve (CH))
Co-authors
Tobias Golling
(Universite de Geneve (CH))
Andrea Coccaro
(University of Geneva)
