CaloDVAE : Discrete Variational Autoencoders for Fast Calorimeter Shower Simulation

• URL: http://arxiv.org/abs/2210.07430v1
• Date: Fri, 14 Oct 2022 00:18:40 GMT
• Title: CaloDVAE : Discrete Variational Autoencoders for Fast Calorimeter Shower Simulation
• Authors: Abhishek Abhishek, Eric Drechsler, Wojciech Fedorko, Bernd Stelzer
• Abstract summary: Calorimeter simulation is the most computationally expensive part of Monte Carlo generation of samples. We present a technique based on Discrete Variational Autoencoders (DVAEs) to simulate particle showers in Electromagnetic Calorimeters.
• Score: 2.0646127669654826
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Calorimeter simulation is the most computationally expensive part of Monte Carlo generation of samples necessary for analysis of experimental data at the Large Hadron Collider (LHC). The High-Luminosity upgrade of the LHC would require an even larger amount of such samples. We present a technique based on Discrete Variational Autoencoders (DVAEs) to simulate particle showers in Electromagnetic Calorimeters. We discuss how this work paves the way towards exploration of quantum annealing processors as sampling devices for generation of simulated High Energy Physics datasets.

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