Deep Generative Models for Fast Photon Shower Simulation in ATLAS

The ATLAS collaboration

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

The need for large-scale production of highly accurate simulated event samples for the extensive physics programme of the ATLAS experiment at the Large Hadron Collider motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms, variational autoencoders and generative adversarial networks are investigated for modelling the response of the central region of the ATLAS electromagnetic calorimeter to photons of various energies. The properties of synthesised showers are compared with showers from a full detector simulation using geant4. Both variational autoencoders and generative adversarial networks are capable of quickly simulating electromagnetic showers with correct total energies and stochasticity, though the modelling of some shower shape distributions requires more refinement. This feasibility study demonstrates the potential of using such algorithms for ATLAS fast calorimeter simulation in the future and shows a possible way to complement current simulation techniques.

Original languageEnglish
Article number7
JournalComputing and Software for Big Science
Volume8
Issue number1
DOIs
Publication statusPublished - Dec 2024

ASJC Scopus subject areas

  • Software
  • Computer Science (miscellaneous)
  • Nuclear and High Energy Physics

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