Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter

TL;DR

This paper introduces a graph neural network-based method for reconstructing hadronic shower energies in the CMS HGCAL, improving accuracy by addressing fluctuations and energy leakage effects.

Contribution

It presents a novel graph neural network approach with a dynamic reduction architecture for hadronic shower reconstruction in high granularity calorimeters.

Findings

Effective mitigation of shower fluctuation effects.

Improved energy reconstruction accuracy.

Demonstrated robustness against energy leakage.

Abstract

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated.