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

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 simu-
lation 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 gen-
erating 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),
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to simulation-based inference and fine-grained density estimation.