Asymmetry Information for Energy Measurement Calibration in a Particle Detector using Decision Trees

Authors

  • Caroline Ribeiro Figueredo Lab. de Sistemas Digitais - PPGEE/UFBA
  • Ricardo Messala M. Costa Lab. de Sistemas Digitais - PPGEE/UFBA
  • Paulo C. M. A. Farias Lab. de Sistemas Digitais - PPGEE/UFBA
  • Eduardo F. de Simas Filho Lab. de Sistemas Digitais - PPGEE/UFBA
  • Edmar de Souza Lab. de Sistemas Digitais - PPGEE/UFBA
  • Juan L. Marin Coordenação de Eletrônica - IFBA - Campus Vitória da Conquista
  • José M. Seixas Lab. de Processamento de Sinais - PEE-Coppe / Poli, UFRJ
  • Bertrand Laforge LPNHE - Sorbonne Université, Paris

Keywords:

ATLAS Experiment, Decision Trees, Energy Calibration, Machine learning, Particle detector

Abstract

The ATLAS detector (A Toroidal LHC ApparatuS), part of the LHC (Large Hadron Collider), represents a milestone in engineering, designed to detect and classify subatomic particles, composed of six subsystems, including the calorimeter. This work investigated energy calibration in the ATLAS online trigger using a regressor based on Gradient Boosted Decision Tree (GBDT), which describes the asymmetries in energy deposition (structures called Quarter Rings). The results indicate that the proposed asymmetry measures are effective and could enhance the energy calibration system.

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Published

2024-10-18

Issue

Vol. 4 No. 1 (2024): CBA2024

Section

Articles