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.

翻译：在高粒度量能器中模拟粒子簇射是机器学习在粒子物理应用中的一个关键前沿领域。利用生成式机器学习模型实现高精度与高速度，能够增强传统模拟方法并缓解主要计算瓶颈。近期研究表明，基于扩散的生成式簇射模拟方法通过生成几何无关的点云而非依赖固定结构，展现出显著优势。我们在先前为国际大型探测器ILD高粒度电磁量能器中电磁簇射模拟所开发的架构基础上，提出了一种基于Transformer的扩展方案。注意力机制使我们能够生成具有更显著子结构的复杂强子簇射，覆盖电磁与强子量能器。这是首次在高度粒度的成像量能器系统中，采用机器学习方法对电磁与强子量能器中的簇射进行整体生成。