CaloDVAE : Discrete Variational Autoencoders for Fast Calorimeter Shower Simulation
This addresses the high computational cost of Monte Carlo simulations in high-energy physics, particularly for the High-Luminosity LHC upgrade, though it appears incremental as an adaptation of existing DVAE methods to a specific domain.
The authors tackled the computational bottleneck of calorimeter shower simulation for Large Hadron Collider data analysis by developing a Discrete Variational Autoencoder (DVAE) technique, achieving faster simulation with potential for quantum annealing integration.
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.