Enhancing Photon Identification with Neural Network Methods

Abstract

We investigate photon--pion discrimination in regimes where electromagnetic showers overlap at the scale of calorimeter granularity. Using full detector simulations with fine-grained calorimeter segmentation of approximately $0.025\times 0.025$ in $(\hat{I}\cdot ,\ddot{I}•)$, we benchmark three approaches: boosted decision trees (BDTs) on shower-shape variables, dense neural networks (DNNs) on the same features, and a ResNet-based convolutional neural network operating directly on calorimeter cell energies. The ResNet significantly outperformed both baseline methods, achieving further gains when augmented with soft scoring and an auxiliary $\hat{I}”R$ regression head. Our results demonstrate that residual convolutional architectures, combined with physics-informed loss functions, can substantially improve photon identification in high-luminosity collider environments in which overlapping electromagnetic showers challenge traditional methods.