Design of a SiPM-on-Tile ZDC for the future EIC and its Performance with Graph Neural Networks

TL;DR

This paper introduces a SiPM-on-tile zero-degree calorimeter design for the EIC, utilizing graph neural networks to improve energy and angle measurements, surpassing current performance requirements.

Contribution

It presents a novel high-granularity ZDC design with a staggered-layer arrangement and demonstrates the effectiveness of GNNs in optimizing calorimeter performance.

Findings

GNNs significantly improve energy and angle regression accuracy.

The design meets and exceeds EIC performance requirements.

GNNs outperform traditional methods in complex geometries.

Abstract

We present a design for a high-granularity zero-degree calorimeter (ZDC) for the upcoming Electron-Ion Collider (EIC). The design uses SiPM-on-tile technology and features a novel staggered-layer arrangement that improves spatial resolution. To fully leverage the design's high granularity and non-trivial geometry, we employ graph neural networks (GNNs) for energy and angle regression as well as signal classification. The GNN-boosted performance metrics meet, and in some cases, significantly surpass the requirements set in the EIC Yellow Report, laying the groundwork for enhanced measurements that will facilitate a wide physics program. Our studies show that GNNs can significantly enhance the performance of high-granularity CALICE-style calorimeters by automating and optimizing the software compensation algorithms required for these systems. This improvement holds true even in the case of complicated geometries that pose challenges for image-based AI/ML methods.