Deep generative models for fast photon shower simulation in ATLAS

TL;DR

This paper explores deep learning models, specifically variational autoencoders and generative adversarial networks, to rapidly simulate electromagnetic showers in the ATLAS detector, aiming to improve simulation efficiency and accuracy.

Contribution

It demonstrates the feasibility of using deep generative models for fast calorimeter simulation in ATLAS, offering a potential complement to traditional methods.

Findings

Models can simulate showers with correct energies and stochasticity

Some shower shape distributions need further refinement

Deep generative models show promise for future ATLAS simulations

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.