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

Baran Hashemi1, Nikolai Hartmann2, Sahand Sharifzadeh3

  • 1ORIGINS Data Science Lab, Technical University Munich, Munich, Germany. baran.hashemi@origins-cluster.de.

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|June 8, 2024
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Summary
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We developed a new AI model, Intra-Event Aware Generative Adversarial Network (IEA-GAN), to efficiently simulate complex particle detector responses. This advances high-granularity detector simulation for particle physics research.

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Area of Science:

  • Particle Physics
  • Artificial Intelligence
  • High-Energy Physics

Background:

  • Simulating high-resolution detector responses is computationally demanding.
  • Existing generative models struggle with the correlated, fine-grained data in ultra-high-granularity detectors.

Purpose of the Study:

  • To develop an efficient method for simulating ultra-high-granularity detector responses.
  • To address limitations in current generative models for complex detector data.

Main Methods:

  • Introduced the Intra-Event Aware Generative Adversarial Network (IEA-GAN).
  • Utilized a Transformer-based Relational Reasoning Module for event approximation.
  • Implemented Self-Supervised intra-event aware and Uniformity loss functions.

Main Results:

  • IEA-GAN generates contextualized, high-resolution detector responses.
  • Achieved enhanced sample fidelity and diversity in simulations.
  • Successfully applied to the Belle II Pixel Vertex Detector (PXD) with millions of channels.

Conclusions:

  • IEA-GAN offers a novel approach to high-granularity detector simulation.
  • This method has potential applications in Foundation Models for future colliders like HL-LHC.
  • Enables advancements in simulation-based inference and density estimation.