Deep Learning for jets: towards a unified jet algorithm

• Alexandre De Moor (VUB)

The steady progress in machine learning leads to substantial performance improvements in various areas of high-energy physics, especially for object identification. Jet flavor identification (tagging) is a prominent benchmark that profits from elaborate architectures, leveraging information from low-level input variables and their correlations. Throughout the data-taking eras of the Large Hadron Collider (LHC) (Run 1 - Run 3), various deep-learning-based algorithms were established and led to a significantly improved tagging performance of heavy flavor jets, originating from the hadronization of b and c quarks. In recent years, new approaches for object tagging based on jets, unified different approaches and extended the paradigm of b and c jets identification to s jets and hadronic τ jets identification, simultaneously with a flavor aware jet energy and resolution regression, and incorporating an innovative adversarial training approach in order to reduce the dependence on possible mismodeling in simulation compared to data.