Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning
arxiv(2023)
摘要
Simulating high-resolution detector responses is a storage-costly and
computationally intensive process that has long been challenging in particle
physics. Despite the ability of deep generative models to make this process
more cost-efficient, ultra-high-resolution detector simulation still proves to
be difficult as it contains correlated and fine-grained mutual information
within an event. To overcome these limitations, we propose Intra-Event Aware
GAN (IEA-GAN), a novel fusion of Self-Supervised Learning and Generative
Adversarial Networks. IEA-GAN presents a Relational Reasoning Module that
approximates the concept of an ”event” in detector simulation, allowing for
the generation of correlated layer-dependent contextualized images for
high-resolution detector responses with a proper relational inductive bias.
IEA-GAN also introduces a new intra-event aware loss and a Uniformity loss,
resulting in significant enhancements to image fidelity and diversity. We
demonstrate IEA-GAN's application in generating sensor-dependent images for the
high-granularity Pixel Vertex Detector (PXD), with more than 7.5M information
channels and a non-trivial geometry, at the Belle II Experiment. Applications
of this work include controllable simulation-based inference and event
generation, high-granularity detector simulation such as at the HL-LHC (High
Luminosity LHC), and fine-grained density estimation and sampling. To the best
of our knowledge, IEA-GAN is the first algorithm for faithful
ultra-high-resolution detector simulation with event-based reasoning.
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