Analysis of Testbeam Data Recorded with the Large CALICE AHCAL Technological Prototype

TL;DR

This paper discusses the analysis of data from a large, highly granular prototype of the CALICE AHCAL calorimeter, focusing on shower studies, particle flow, hit timing, and machine learning applications.

Contribution

It presents the first detailed analysis of testbeam data from a large CALICE AHCAL prototype with advanced granularity and multiple analysis techniques.

Findings

Detailed shower shape studies enabled by high granularity

Effective particle separation using PandoraPFA algorithm

Potential for machine learning applications in calorimeter data analysis

Abstract

The Analog Hadron Calorimeter (AHCAL) concept developed by the CALICE collaboration is a highly granular sampling calorimeter with \SI{3x3}{\square\centi\meter} plastic scintillator tiles individually read out by silicon photomultipliers (SiPMs) as active material. We have built a large scalable engineering prototype with 38 layers in a steel absorber structure with a thickness of ~4 interaction length. The prototype was exposed to electron, muon and hadron beams at the DESY and CERN testbeam facilities in 2018. The high granularity of the detector allows detailed studies of shower shapes and shower separation with the PandoraPFA particle flow algorithm as well as studies of hit times. The large amount of information is also an ideal place for the application of machine learning algorithms. This article provides an overview of the ongoing analyses.