Generation of simulated detector response to collision products is crucial to data analysis in particle physics, but computationally very expensive. One subdetector, the calorimeter, dominates the computational time due to the high granularity of its cells and complexity of the interactions. Generative models can provide more rapid sample production, but currently require significant effort to optimize performance for specific detector geometries, often requiring many models to describe the varying cell sizes and arrangements, without the ability to generalize to other geometries. We develop a $\textit{geometry-aware}$ autoregressive model, which learns how the calorimeter response varies with geometry, and is capable of generating simulated responses to unseen geometries without additional training. The geometry-aware model outperforms a baseline unaware model by over $50\%$ in several metrics such as the Wasserstein distance between the generated and the true distributions of key quantities which summarize the simulated response. A single geometry-aware model could replace the hundreds of generative models currently designed for calorimeter simulation by physicists analyzing data collected at the Large Hadron Collider. For the study of future detectors, such a foundational model will be a crucial tool, dramatically reducing the large upfront investment usually needed to develop generative calorimeter models.

翻译：模拟探测器对碰撞产物的响应是粒子物理学数据分析的关键，但其计算成本极高。其中，子探测器——量热器因其单元的高粒度及相互作用的复杂性而主导了计算时间。生成模型能够实现更快速的样本生成，但当前针对特定探测器几何结构优化性能需要大量工作，且通常需要多个模型来描述不同的单元尺寸和排列方式，且无法推广至其他几何结构。我们开发了一种几何感知自回归模型，该模型能学习量热器响应如何随几何结构变化，并能在无需额外训练的情况下生成对未见几何结构的模拟响应。在多个评价指标（如生成分布与真实分布之间的Wasserstein距离）上，该几何感知模型的表现比基线无感知模型提升了50%以上，这些指标综合反映了模拟响应的关键量。单个几何感知模型可取代目前为分析大型强子对撞机数据的物理学家设计的数百个用于量热器模拟的生成模型。对于未来探测器的研究，此类基础模型将成为关键工具，大幅减少通常开发生成式量热器模型所需的前期投入。