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.
- Publication:
-
Journal of Physics Conference Series
- Pub Date:
- April 2020
- DOI:
- 10.1088/1742-6596/1525/1/012077
- arXiv:
- arXiv:2210.06204
- Bibcode:
- 2020JPhCS1525a2077G
- Keywords:
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- High Energy Physics - Experiment;
- Physics - Instrumentation and Detectors
- E-Print:
- 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/