Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with generative machine learning models would enable them to augment traditional simulations and alleviate a major computing constraint. This work achieves a major breakthrough in this task by, for the first time, directly generating a point cloud of a few thousand space points with energy depositions in the detector in 3D space without relying on a fixed-grid structure. This is made possible by two key innovations: i) using recent improvements in generative modeling we apply a diffusion model to generate ii) an initial even higher-resolution point cloud of up to 40,000 so-called Geant4 steps which is subsequently down-sampled to the desired number of up to 6,000 space points. We showcase the performance of this approach using the specific example of simulating photon showers in the planned electromagnetic calorimeter of the International Large Detector (ILD) and achieve overall good modeling of physically relevant distributions.

翻译：在高粒度探测器中模拟粒子簇射是机器学习应用于粒子物理领域的关键前沿方向。通过生成式机器学习模型实现高精度与快速模拟，有望增强传统模拟方法并缓解计算瓶颈。本研究在该任务中取得重大突破：首次在无需依赖固定网格结构的情况下，直接生成包含数千个三维空间点且带有探测器能量沉积信息的点云。这一成果得益于两项关键创新：i) 利用生成建模领域的最新进展，应用扩散模型生成；ii) 初始甚至更高分辨率的点云（包含多达40,000个所谓的Geant4步进点），随后通过降采样至所需数量的最多6,000个空间点。我们以国际大型探测器（ILD）计划中的电磁量能器为例，展示了该方法模拟光子簇射的性能，并在整体上实现了对物理相关分布的良好建模。