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Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning

基于事件内感知GAN和自监督关系推理的超高分辨率探测器仿真

作者: Hosein Hashemi,Nikolai Hartmann,Sahand Sharifzadeh,James Kahn,Thomas Kuhr

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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.

模拟高分辨率探测器的响应是一种存储-成本高昂且 计算密集的过程,长期以来一直是粒子领域的挑战 物理学。尽管深度生成模型有能力使这一过程 更具成本效益的超高分辨率探测器模拟仍然证明 因为它包含相关和细粒度的互信息,所以很难 在一个事件中。为了克服这些限制,我们提出了事件内感知 GAN(IEA-GAN),一种新型的自监督学习和产生式学习的融合 对抗性网络。IEA-GAN提出了一个关系推理模块 近似探测器模拟中的“事件”的概念,允许 生成相关的依赖于层的上下文图像 具有适当关系的感应偏置的高分辨率探测器响应。 IEA-GAN还引入了新的事件内感知损失和一致性损失, 从而显著增强了图像的保真度和多样性。我们 演示IEA-GAN在生成传感器相关图像方面的应用 高粒度像素顶点检测器(PXD),信息量超过7.5M 通道和一个不平凡的几何学,在百丽II实验中。应用 包括可控的基于模拟的推理和事件 生成、高粒度探测器模拟,例如在HL-LHC(High 光度LHC),以及细粒度密度估计和采样。致最好的 据我们所知,IEA-GAN是FIRIFY的第一个算法 基于事件推理的超高分辨率探测器仿真。

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