Optimising longitudinal and lateral calorimeter granularity for software compensation in hadronic showers using deep neural networks

TL;DR

This study explores how calorimeter segmentation affects software compensation for hadronic showers, using neural networks to optimize energy measurement accuracy and resolution.

Contribution

It introduces a neural network-based energy regression method that optimizes calorimeter segmentation for improved hadronic shower energy reconstruction.

Findings

Optimal segmentation sizes are ≤0.5 and ≤1.3 nuclear interaction lengths longitudinally and transversely.

Achieved an intrinsic energy resolution of 8%/√E for pion showers.

Segmentation impacts the accuracy of energy measurement in calorimeters.

Abstract

We investigate the effect of longitudinal and transverse calorimeter segmentation on event-by-event software compensation for hadronic showers. To factorize out sampling and electronics effects, events are simulated in which a single charged pion is shot at a homogenous lead glass calorimeter, split into longitudinal and transverse segments of varying size. As an approximation of an optimal reconstruction, a neural network-based energy regression is trained. The architecture is based on blocks of convolutional kernels customized for shower energy regression using local energy densities; biases at the edges of the training dataset are mitigated using a histogram technique. With this approximation, we find that a longitudinal and transverse segment size less than or equal to 0.5 and 1.3 nuclear interaction lengths, respectively, is necessary to achieve an optimal energy measurement. In addition, an intrinsic energy resolution of $8\%/\sqrt{E}$ for pion showers is observed.