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

Author

Listed:
  • Baran Hashemi

    (Technical University Munich)

  • Nikolai Hartmann

    (Ludwig Maximilians University in Munich)

  • Sahand Sharifzadeh

    (Ludwig Maximilians University in Munich)

  • James Kahn

    (Helmholtz AI
    Karlsruhe Institute of Technology (KIT))

  • Thomas Kuhr

    (Ludwig Maximilians University in Munich)

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.

Suggested Citation

  • Baran Hashemi & Nikolai Hartmann & Sahand Sharifzadeh & James Kahn & Thomas Kuhr, 2024. "Ultra-high-granularity detector simulation with intra-event aware generative adversarial network and self-supervised relational reasoning," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49104-4
    DOI: 10.1038/s41467-024-49104-4
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    References listed on IDEAS

    as
    1. Po-Ssu Huang & Scott E. Boyken & David Baker, 2016. "The coming of age of de novo protein design," Nature, Nature, vol. 537(7620), pages 320-327, September.
    2. Sydney Otten & Sascha Caron & Wieske de Swart & Melissa van Beekveld & Luc Hendriks & Caspar van Leeuwen & Damian Podareanu & Roberto Ruiz de Austri & Rob Verheyen, 2021. "Event generation and statistical sampling for physics with deep generative models and a density information buffer," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
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