Optimisation of a silicon-tungsten electromagnetic calorimeter energy response to photons

TL;DR

This paper presents ML-based methods to improve the energy resolution of silicon-tungsten electromagnetic calorimeters, leading to a 20% enhancement and better energy leakage correction, facilitating design reoptimization for future collider detectors.

Contribution

It introduces machine learning techniques for SiW-ECAL reconstruction, significantly improving performance and enabling design reoptimization for circular collider environments.

Findings

Achieved approximately 20% improvement in low-energy resolution.

Effectively corrected energy leakage at high energies.

Enabled reoptimization of SiW-ECAL design based on ML methods.

Abstract

An innovative path for the detectors at future colliders to achieve higher performances is to use a Particle Flow approach, which requires highly granular calorimeters to image individual showers. The silicon-tungsten electromagnetic calorimeter (SiW-ECAL) aims at fulfilling all the expected physical and technical requirements. SiW-ECAL has been developed by the CALICE and ILD collaborations for more than two decades and is now reaching maturity, for linear machines. However, with the tendency towards circular machines, the progress of electronics and the rapid advancement of machine learning (ML) techniques, the SiW-ECAL design needs to be reoptimised to enhance its performance. This study develops ML-based reconstruction approaches for SiW-ECAL, achieving an approximate 20% improvement in energy resolution in the low-energy range and effectively correcting energy leakage in the high-energy range. Subsequently, the SiW-ECAL design is reoptimized based on this method.