Open AccessDeep generative models for fast shower simulation in ATLAS
Author(s) -
A. Ghosh
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1525/1/012077
Subject(s) - atlas (anatomy) , interpolation (computer graphics) , calorimeter (particle physics) , computer science , encoder , high fidelity , detector , range (aeronautics) , autoencoder , generative grammar , deep learning , physics , algorithm , artificial intelligence , nuclear physics , optics , aerospace engineering , image (mathematics) , acoustics , engineering , paleontology , biology , operating system
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.