Simultaneous Jet Energy and Mass Calibrations with Neural Networks

The jet mass is one of the most important observables for identifying boosted, hadronically decaying, massive particles. ATLAS has historically calibrated the jet mass after calibrating the jet energy independently of jet mass. As the jet energy response depends on the jet mass, this sequential approach can lead to a non-closure in the jet energy calibration. This note illustrates how to simultaneously calibrate the jet energy and jet mass within the generalized numerical inversion framework. As the jet mass response often has long asymmetric tails, traditional regression techniques can be biased away from the mode. In addition to the simultaneous energy and mass calibration, this note also uses a tailored loss function to directly learn the mode of the response.