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arXiv 2402.11575hep-exhep-ph
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CaloGraph:用于不规则几何结构量能器中快速簇射生成的基于图的扩散模型

CaloGraph: Graph-based diffusion model for fast shower generation in calorimeters with irregular geometry

Dmitrii Kobylianskii, Nathalie Soybelman, Etienne Dreyer, Eilam Gross

AI总结:

针对高能物理实验中探测器模拟的计算挑战,本研究提出新型基于图的扩散模型CaloGraph,用于不规则几何低颗粒度探测器的快速量能器模拟,并在ATLAS数据集上实现粒子物理领域首次应用。

摘要:

去噪扩散模型在各类生成任务中备受瞩目,促使人们探索其在量能器响应生成中的应用。鉴于高能物理实验中探测器模拟带来的计算挑战,探索新的基于机器学习的方法势在必行。本研究提出了一种专为快速量能器模拟设计的新型基于图的扩散模型。该方法特别适用于具有不规则几何结构的低颗粒度探测器。我们将该模型应用于在2022年快速量能器模拟挑战赛背景下发布的ATLAS数据集,标志着图扩散模型在粒子物理领域的首次应用。

英文摘要:

Denoising diffusion models have gained prominence in various generative tasks, prompting their exploration for the generation of calorimeter responses. Given the computational challenges posed by detector simulations in high-energy physics experiments, the necessity to explore new machine-learning-based approaches is evident. This study introduces a novel graph-based diffusion model designed specifically for rapid calorimeter simulations. The methodology is particularly well-suited for low-granularity detectors featuring irregular geometries. We apply this model to the ATLAS dataset published in the context of the Fast Calorimeter Simulation Challenge 2022, marking the first application of a graph diffusion model in the field of particle physics.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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