Speaker
Robert Les
(Michigan State University (US))
Description
Hadronic object reconstruction & classification is one of the most promising settings for cutting-edge machine learning and artificial intelligence algorithms at the LHC. In this contribution, recent highlights of ML applications by ATLAS for boosted-object identification will be presented. This covers results of constituent-based transformers for quark-gluon, top-quark, and W boson discrimination, as well as detailed studies in performance of multi-class taggers, MC generator dependencies, and polarization-aware tagging.
Authors
Jeff Dandoy
(Carleton University (CA))
Pierre Antoine Delsart
Robert Les
(Michigan State University (US))