Skip to Main content Skip to Navigation
Conference papers

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

S. Mannai K. Manai E. Cortina I. Laktineh 1
1 IPNL - Institut de Physique Nucléaire de Lyon
Abstract : The Semi-Digital Hadronic CALorimeter (SD-HCAL) is one of the two hadronic calorimeter options proposed by the International Large Detector (ILD) project for the future International Linear Collider experiments (ILC). It is a sampling calorimeter with 48 Glass Resistive Plate Chambers (GRPCs) finely segmented into cells of 1 cm2 ensuring a high granularity which is required for the application of the Particle Flow Algorithm (PFA) in order to improve the jet energy resolution which is the corner stone of ILC experiments. The performance of the SDHCAL technological prototype was tested successfully in beam tests at CERN several times. The main point to be discussed concerns the energy reconstruction in SDHCAL. Based on Monte Carlo Simulation of the SDHCAL prototype with Geant4, we will show different analytic energy reconstruction methods used to study the single particle energy resolution and the linearity of the detecor response to pions. In particular, we will highlight a new technique based on the Artificial Neural Network giving promising results compared to the classical analytic methods.
Document type :
Conference papers
Complete list of metadatas
http://hal.in2p3.fr/in2p3-01331153
Contributor : Dominique Girod <>
Submitted on : Monday, June 13, 2016 - 3:08:22 PM
Last modification on : Tuesday, November 19, 2019 - 2:45:05 AM

Identifiers

Collections

IN2P3 | CNRS | UNIV-LYON1 | IP2I | UDL

Citation

S. Mannai, K. Manai, E. Cortina, I. Laktineh. Energy reconstruction in a highly granularity semi-digital hadronic calorimeter for ILC experiments. 4th International Conference on Advancements in Nuclear Instrumentation Measurement Methods and their Applications (ANIMMA 2015), Apr 2015, Lisbon, Portugal. pp.1-7, ⟨10.1109/ANIMMA.2015.7465286⟩. ⟨in2p3-01331153⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

101