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arXiv · 2303.18150

New Angles on Fast Calorimeter Shower Simulation

Sascha Diefenbacher·Engin Eren·Frank Gaede·Gregor Kasieczka·Anatolii Korol·Katja Krüger·Peter McKeown·Lennart Rustige
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Abstract

The demands placed on computational resources by the simulation requirements of high energy physics experiments motivate the development of novel simulation tools. Machine learning based generative models offer a solution that is both fast and accurate. In this work we extend the Bounded Information Bottleneck Autoencoder (BIB-AE) architecture, designed for the simulation of particle showers in highly granular calorimeters, in two key directions. First, we generalise the model to a multi-parameter conditioning scenario, while retaining a high degree of physics fidelity. In a second step, we perform a detailed study of the effect of applying a state-of-the-art particle flow-based reconstruction procedure to the generated showers. We demonstrate that the performance of the model remains high after reconstruction. These results are an important step towards creating a more general simulation tool, where maintaining physics performance after reconstruction is the ultimate target.

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Sascha Diefenbacher, Engin Eren, Frank Gaede, Gregor Kasieczka, Anatolii Korol, Katja Krüger, Peter McKeown, Lennart Rustige. 2023-03-31. New Angles on Fast Calorimeter Shower Simulation. https://arxiv.org/abs/2303.18150

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