Energy reconstruction in a highly granularity semi-digital hadronic calorimeter for ILC experiments

Abstract

The Semi-Digital Hadronic CALorimeter (SDHCAL) is one of the two hadronic calorimeter options proposed by the International Large Detector (ILD) project for the future International Linear Collider (ILC) experiments. It is a sampling calorimeter with 48 active layers made of Glass Resistive Plate Chambers (GRPCs) and their embedded electronics. A fine lateral segmentation is obtained thanks to pickup pads of 1 cm2. This ensures the high granularity required for the application of the Particle Flow Algorithm (PFA) in order to improve the jet energy resolution in the ILC experiments. The performance of the SDHCAL technological prototype was tested successfully in several beam tests at CERN. The main point to be discussed here concerns the energy reconstruction in SDHCAL. Based on Monte Carlo simulation of the SDHCAL prototype using the GEANT4 package, we present different energy reconstruction methods to study the energy linearity and resolution of the detector response to single hadrons. In particular, we highlight a new technique based on the Artificial Neural Network giving promising results compared to analytic methods.