DeXTer: Deep Set based Neural Networks for boosted multiple b-jet identification in ATLAS
Yuan-Tang Chou1*, Rafael Coelho Lopes de Sa1, Rafael Teixeira de Lima2, Verena Ingrid Martinez Outschoorn1, Aurelio Juste Rozas3
1Department of Physics, University of Massachusetts Amherst, Amherst, MA, USA
2SLAC National Accelerator Laboratory, Stanford, CA, USA
3Institut de Fisica d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Barcelona, Spain
* Presenter:Yuan-Tang Chou,
This work presents algorithms for flavor tagging identification of jets that are initiated by one or two independent heavy-flavor hadrons. Algorithms in ATLAS for hadronic jets typically focus on high transverse momentum, above 200 GeV. This work describes the first implementation of a double-b tagger for low transverse momentum jets, below 200 GeV. This algorithm relies on large radius track-jets which can be defined at low transverse momenta and implements a deep-set neural network that uses displaced tracks, secondary vertices, and substructure information to identify the presence of multiple heavy-flavored hadrons.

Keywords: B-tagging, Large Hadron Collider, Jet identification