The Optimal use of Segmentation for Sampling Calorimeters

TL;DR

This study uses deep neural networks to evaluate how calorimeter segmentation affects energy reconstruction, showing that finer segmentation improves resolution for isolated charged pion showers in a simulated detector for the Electron Ion Collider.

Contribution

The paper introduces a neural network-based approach to assess the impact of segmentation on calorimeter performance, providing a benchmark for detector optimization.

Findings

Finer longitudinal segmentation improves energy resolution.

Deep neural networks effectively utilize all available calorimeter information.

Achieves better than 10% energy resolution for charged pion showers.

Abstract

One of the key design choices of any sampling calorimeter is how fine to make the longitudinal and transverse segmentation. To inform this choice, we study the impact of calorimeter segmentation on energy reconstruction. To ensure that the trends are due entirely to hardware and not to a sub-optimal use of segmentation, we deploy deep neural networks to perform the reconstruction. These networks make use of all available information by representing the calorimeter as a point cloud. To demonstrate our approach, we simulate a detector similar to the forward calorimeter system intended for use in the ePIC detector, which will operate at the upcoming Electron Ion Collider. We find that for the energy estimation of isolated charged pion showers, relatively fine longitudinal segmentation is key to achieving an energy resolution that is better than 10% across the full phase space. These results provide a valuable benchmark for ongoing EIC detector optimizations and may also inform future studies involving high-granularity calorimeters in other experiments at various facilities.