  The liquid argon ionization current in a sampling calorimeter cell can be
analyzed to determine the energy of detected particles. In practice,
experimental artifacts such as pileup and electronic noise make the inference
of energy from current a difficult process. The beam intensity of the Large
Hadron Collider will be significantly increased during the Phase-II long shut
down of 2025-2027. Signal processing techniques that are used to extract the
energy of detected particles in the ATLAS detector will suffer a significant
loss in performance under these conditions. This paper compares the presently
used optimal filter technique to convolutional neural networks for energy
reconstruction in the ATLAS liquid argon hadronic end cap calorimeter. In
particular, it is shown that convolutional neural networks trained with an
appropriately tuned and novel loss function are able to outperform the optimal
filter technique.
