Speaker
Description
The identification of jets containing b-hadrons is essential for many physics analyses at the LHC, including precision measurements of Higgs boson and top-quark processes, as well as searches for physics beyond the Standard Model. We present recent improvements in the discrimination of b-jets from jets originating from lighter quarks using the ATLAS detector. These advances are driven by state-of-the-art machine learning techniques based on transformer architectures. Their performance is well modelled by the ATLAS simulation, as demonstrated through dedicated calibration studies, the results of which will be presented. Compared to previous algorithms, the transformer-based approach improves the rejection of c-jets (light-jets) by factors of 3.5 (1.8) at a b-jet tagging efficiency of 70%. We also discuss the latest version of this algorithm and its expected performance at the High-Luminosity LHC (HL-LHC).