Speaker
Description
Several flavor tagging algorithms exist in ATLAS for jets containing two b-hadrons. These double-b tagger algorithms focus on high transverse-momentum jets, usually above 200 GeV. This work describes the development of a new double-b tagger for jets below 200~GeV. The algorithm relies on large radius track-jets which can be reconstructed at low transverse momenta and implements a neural network architecture based on Deep Sets that uses displaced tracks, secondary vertices, and substructure information to identify the presence of multiple $b$-hadrons. A measurement of the efficiency of the algorithm is performed in ttbar and Z + jets events using the collision data from the Large Hadron Collider at sort{s}= 13 TeV center-of-mass energy recorded with the ATLAS detector between 2015 and 2018, corresponding to an integrated luminosity of 139 fb^-1
