Constituent-Based Quark Gluon Tagging using Transformers with the ATLAS detector
Year: 2023
Type: report
Abstract: Jet constituents provide a more complete description of the radiation pattern inside a jet than observables describing the global jet properties like the number of tracks or the jet width. An approach to tag quark- and gluon-initiated jets using their constituents (Particle Flow Objects) reconstructed in ATLAS is presented. Monte Carlo simulations of proton--proton collisions at center of mass energy of 13~TeV are used. Transformer architectures are used to learn long-range dependencies between jet constituent kinematic variables. Several variations of transformers are studied, and their performance is compared to taggers based on global jet properties and other jet constituent taggers. A new Deep Learning architecture (DeParT) is presented, slightly outperforming other networks. All models are evaluated on simulated data from multiple Monte Carlo generators to study their model-dependence.
Author: ATLAS Collaboration
Institution: Deutsches Elektronen-Synchrotron DESY
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6945930335