Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Deep Generative Models for Fast Photon Shower Simulation in ATLAS
AU - The ATLAS collaboration
AU - Barton, A.E.
AU - Bertram, I.A.
AU - Borissov, G.
AU - Bouhova-Thacker, E.V.
AU - Ferrando, James
AU - Fox, H.
AU - Henderson, R.C.W.
AU - Jones, R.W.L.
AU - Kartvelishvili, V.
AU - Love, P.A.
AU - Meng, L.
AU - Muenstermann, D.
AU - Rybacki, K.
AU - Sanderswood, Izaac
AU - Smizanska, M.
AU - Spinali, S.
AU - Wharton, A.M.
AU - Yexley, Melissa
PY - 2024/3/5
Y1 - 2024/3/5
N2 - AbstractThe 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.
AB - AbstractThe 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.
KW - Nuclear and High Energy Physics
KW - Computer Science (miscellaneous)
KW - Software
U2 - 10.1007/s41781-023-00106-9
DO - 10.1007/s41781-023-00106-9
M3 - Journal article
VL - 8
JO - Computing and Software for Big Science
JF - Computing and Software for Big Science
SN - 2510-2036
IS - 1
M1 - 7
ER -