On the Use of Neural Networks for Energy Reconstruction in High-granularity Calorimeters

We studied the performance of the Convolutional Neural Network (CNN) for energy regression in a finely 3D-segmented calorimeter simulated by GEANT4. 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. In this paper, we present the comparison of several reconstruction techniques: a simple energy sum, a dual-readout analog, a CNN, and a GNN with timing information.