Energy Reconstruction in a High Granularity Semi-Digital Hadronic Calorimeter for ILC Experiments
Résumé
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.
Mots clés
Detectors
Energy resolution
Simulation
Linearity
Mesons
Glass
Calorimetry
Artificial neural networks
particle calorimetry
neural nets
high granularity semidigital hadronic calorimeter
international large detector experiments
analytic methods
artificial neural network
single hadron detector response
energy linearity
energy reconstruction methods
GEANT4 package
Monte Carlo simulation
CERN
jet energy resolution
PFA
particle flow algorithm
fine lateral segmentation
embedded electronics
GRPC
glass resistive plate chambers
sampling calorimeter
ILC experiments
international linear collider experiments
SDHCAL technological prototype
simulation
glass RPC
hadronic calorimeter
neural network
semi-digital
calorimeter: hadronic
resistive plate chamber: glass
energy resolution
GEANT
particle flow
ILD detector
performance
data analysis method