  The energy and mass measurements of jets are crucial tasks for the Large
Hadron Collider experiments. This paper presents a new calibration method to
simultaneously calibrate these quantities for large-radius jets measured with
the ATLAS detector using a deep neural network (DNN). To address the
specificities of the calibration problem, special loss functions and training
procedures are employed, and a complex network architecture, which includes
feature annotation and residual connection layers, is used. The DNN-based
calibration is compared to the standard numerical approach in an extensive
series of tests. The DNN approach is found to perform significantly better in
almost all of the tests and over most of the relevant kinematic phase space. In
particular, it consistently improves the energy and mass resolutions, with a
30% better energy resolution obtained for transverse momenta $p_{\text{T}}>500$
GeV.
