  We contrasted the performance of deep neural networks - Convolutional Neural
Network (CNN) and Graph Neural Network (GNN) - to current state of the art
energy regression methods in a finely 3D-segmented calorimeter simulated by
GEANT4. This comparative benchmark gives us some insight to assess the
particular latent signals neural network methods exploit to achieve superior
resolution. A CNN trained solely on a pure sample of pions achieved substantial
improvement in the energy resolution for both single pions and jets over the
conventional approaches. It maintained good performance for electron and photon
reconstruction. We also used the Graph Neural Network (GNN) with edge
convolution to assess the importance of timing information in the shower
development for improved energy reconstruction. We implement a simple
simulation based correction to the energy sum derived from the fraction of
energy deposited in the electromagnetic shower component. This serves as an
approximate dual-readout analogue for our benchmark comparison. Although this
study does not include the simulation of detector effects, such as electronic
noise, the margin of improvement seems robust enough to suggest these benefits
will endure in real-world application. We also find reason to infer that the
CNN/GNN methods leverage latent features that concur with our current
understanding of the physics of calorimeter measurement.
