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Deep Generative Models for Fast Photon Shower Simulation in ATLAS

Aad, G. ; Åkesson, T.P.A. LU orcid ; Corrigan, E.E. LU ; Doglioni, C. LU ; Geisen, J. LU orcid ; Hansen, E. LU ; Hedberg, V. LU ; Jarlskog, G. LU ; Konya, B. LU and Lytken, E. LU orcid , et al. (2024) In Computing and Software for Big Science 8(1).
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... (More)
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. © The Author(s) 2024. (Less)
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author collaboration
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publishing date
type
Contribution to journal
publication status
published
subject
in
Computing and Software for Big Science
volume
8
issue
1
article number
7
publisher
Springer Nature
external identifiers
  • scopus:85189330049
ISSN
2510-2044
DOI
10.1007/s41781-023-00106-9
language
English
LU publication?
yes
additional info
Number of authors = 2858 EID = 85189330049 Article no = 7 Affiliation = Aad G., CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille, France Affiliation = Zou W., Nevis Laboratory, Columbia University, Irvington, NY, United States Affiliation = Zwalinski L., CERN, Geneva, Switzerland
id
02fcf59b-67b3-48b1-b4d4-4bd82943bab2
date added to LUP
2025-12-04 11:32:04
date last changed
2025-12-04 11:33:22
@article{02fcf59b-67b3-48b1-b4d4-4bd82943bab2,
  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. © The Author(s) 2024.}},
  author       = {{Aad, G. and Åkesson, T.P.A. and Corrigan, E.E. and Doglioni, C. and Geisen, J. and Hansen, E. and Hedberg, V. and Jarlskog, G. and Konya, B. and Lytken, E. and Mankinen, K.H. and Marcon, C. and Mjörnmark, J.U. and Mullier, G.A. and Poettgen, R. and Simpson, N.D. and Skorda, E. and Smirnova, O. and Zwalinski, L.}},
  issn         = {{2510-2044}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{Springer Nature}},
  series       = {{Computing and Software for Big Science}},
  title        = {{Deep Generative Models for Fast Photon Shower Simulation in ATLAS}},
  url          = {{http://dx.doi.org/10.1007/s41781-023-00106-9}},
  doi          = {{10.1007/s41781-023-00106-9}},
  volume       = {{8}},
  year         = {{2024}},
}