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The energy and mass measurements of jets are crucial tasks for the LargeHadron Collider experiments. This paper presents a new calibration method tosimultaneously calibrate these quantities for large-radius jets measured withthe ATLAS detector using a deep neural network (DNN). To address thespecificities of the calibration problem, special loss functions and trainingprocedures are employed, and a complex network architecture, which includesfeature annotation and residual connection layers, is used. The DNN-basedcalibration is compared to the standard numerical approach in an extensiveseries of tests. The DNN approach is found to perform significantly better inalmost all of the tests and over most of the relevant kinematic phase space. Inparticular, it consistently improves the energy and mass resolutions, with a30% better energy resolution obtained for transverse momenta $p_{\text{T}}>500$GeV.

Simultaneous energy and mass calibration of large-radius jets with the ATLAS detector using a deep neural network