DeXTer: Deep Sets based Neural Networks for Low-$p_{T}$ $X \rightarrow $ $b\bar{b}$ Identification in ATLAS

Several flavor tagging algorithms exist in ATLAS for jets containing two $b$-hadrons. These $\textit{double-$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 $t\bar{t}$ and $Z+\text{jets}$ events using the collision data from the Large Hadron Collider at $\sqrt{s}$ = 13 TeV center-of-mass energy recorded with the ATLAS detector between 2015 and 2018, corresponding to an integrated luminosity of 139 $\text{fb}^{-1}$.