Ultra-high-granularity detector simulation with intra-event aware generative adversarial network and self-supervised relational reasoning

Baran Hashemi (), Nikolai Hartmann, Sahand Sharifzadeh, James Kahn and Thomas Kuhr
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Baran Hashemi: Technical University Munich
Nikolai Hartmann: Ludwig Maximilians University in Munich
Sahand Sharifzadeh: Ludwig Maximilians University in Munich
James Kahn: Helmholtz AI
Thomas Kuhr: Ludwig Maximilians University in Munich

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: Abstract Simulating high-resolution detector responses is a computationally intensive process that has long been challenging in Particle Physics. Despite the ability of generative models to streamline it, full ultra-high-granularity detector simulation still proves to be difficult as it contains correlated and fine-grained information. To overcome these limitations, we propose Intra-Event Aware Generative Adversarial Network (IEA-GAN). IEA-GAN presents a Transformer-based Relational Reasoning Module that approximates an event in detector simulation, generating contextualized high-resolution full detector responses with a proper relational inductive bias. IEA-GAN also introduces a Self-Supervised intra-event aware loss and Uniformity loss, significantly enhancing sample fidelity and diversity. We demonstrate IEA-GAN’s application in generating sensor-dependent images for the ultra-high-granularity Pixel Vertex Detector (PXD), with more than 7.5 M information channels at the Belle II Experiment. Applications of this work span from Foundation Models for high-granularity detector simulation, such as at the HL-LHC (High Luminosity LHC), to simulation-based inference and fine-grained density estimation.

Date: 2024
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DOI: 10.1038/s41467-024-49104-4

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