Deep generative models for fast photon shower simulation in ATLAS

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.

53 pages in total, 19 figures, published in Computing and Software for Big Science as Comput Softw Big Sci 8, 7 (2024). All figures including auxiliary figures are available at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/SIMU-2020-04/

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• Differentiable MadNIS-Lite
• Anomaly detection with flow-based fast calorimeter simulators
• CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
• PIPPIN: Generating variable length full events from partons
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• BitHEP -- The Limits of Low-Precision ML in HEP
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