Speaker
Description
The precise measurement of kinematic features of jets is key to the physics program of the LHC. The determination of the energy and mass of jets containing bottom quarks 𝑏-jets is particularly difficult given their distinct radiation patterns and production of undetectable neutrinos via leptonic heavy flavor decays. This talk will describe a novel calibration technique for the b-jet kinematics using transformer-based neural networks trained on simulation samples. Separate simulation-based regression methods have been developed to estimate the transverse momentum of small-radius jets and the transverse momentum and mass of large-radius jets. In both cases, the medians of reconstructed jet properties are corrected to the true value across a range of jet features. A relative jet energy resolution improvement with respect to the nominal calibration between 18% and 31% is demonstrated for small-radius jets. Both the large-radius jet transverse momentum and mass resolution are shown to improve by 25–35%. These methods improve meaningfully upon simulation-based b-jet correction strategies previously used in ATLAS.
