Deep generative models for fast shower simulation in ATLAS

Abstract

The need for large scale and high fidelity simulated samples for the ATLAS experiment motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms at interpolation as well as image generation, Variational Auto-Encoders and Generative Adversarial Networks are investigated for modeling the response of the electromagnetic calorimeter for photons in a central calorimeter region over a range of energies. The synthesized showers are compared to showers from a full detector simulation using Geant4. This study demonstrates the potential of using such algorithms for fast calorimeter simulation for the ATLAS experiment in the future.