用扩散模型生成高粒度量能器中的强子簇射，实现跨电磁与强子量能器的全系统仿真。
CaloHadronic: a diffusion model for the generation of hadronic showers
- 基于Transformer的扩散模型，生成无固定结构的点云簇射。
- 首次实现电磁与强子量能器中复杂子结构的联合生成。
- 适用于高粒度量能器系统，适合粒子物理仿真加速场景。
在高粒度量能器中模拟粒子簇射是机器学习应用于粒子物理的关键前沿。高精度且快速的生成式机器学习模型可增强传统仿真，缓解计算瓶颈。近期研究显示，基于扩散的生成方法不依赖固定结构，而是生成几何无关的点云，效率很高。本文提出一种基于Transformer的扩展架构，用于国际大型探测器（ILD）中高粒度电磁量能器的强子簇射仿真。注意力机制使模型能生成包含更明显子结构的复杂强子簇射，覆盖电磁与强子量能器。这是首次利用机器学习方法在高粒度成像量能器系统中实现跨电磁与强子量能器的完整簇射生成。
原文摘要 · Abstract (English)
Simulating showers of particles in highly-granular calorimeters is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with generative machine learning models can enable them to augment traditional simulations and alleviate a major computing constraint. Recent developments have shown how diffusion based generative shower simulation approaches that do not rely on a fixed structure, but instead generate geometry-independent point clouds, are very efficient. We present a transformer-based extension to previous architectures which were developed for simulating electromagnetic showers in the highly granular electromagnetic calorimeter of the International Large Detector, ILD. The attention mechanism now allows us to generate complex hadronic showers with more pronounced substructure across both the electromagnetic and hadronic calorimeters. This is the first time that machine learning methods are used to holistically generate showers across the electromagnetic and hadronic calorimeter in highly granular imaging calorimeter systems.
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