BibTeX

@article{2303.08046v2,
Author        = {Baran Hashemi and Nikolai Hartmann and Sahand Sharifzadeh and James Kahn and Thomas Kuhr},
Title         = {Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning},
Eprint        = {2303.08046v2},
DOI           = {10.1038/s41467-024-49104-4},
ArchivePrefix = {arXiv},
PrimaryClass  = {physics.ins-det},
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 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. To our knowledge, IEA-GAN is the first algorithm for faithful ultra-high-granularity full detector simulation with event-based reasoning.},
Year          = {2023},
Month         = {Mar},
Note          = {volume 15, Article number: 4916 (2024)},
Url           = {http://arxiv.org/abs/2303.08046v2},
File          = {2303.08046v2.pdf}
}