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 interaction. Generative models can provide more rapid sample production, but currently require significant effort to optimize performance for specific detector geometries, often requiring many networks to describe the varying cell sizes and arrangements, which do not generalize to other geometries. We develop a {\it 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 50\% in metrics such as the Wasserstein distance between generated and 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：对碰撞产物进行模拟探测器响应生成是粒子物理数据分析的关键环节，但计算成本极其高昂。其中，子探测器——量热器——因其单元高粒度和相互作用的复杂性而主导计算时间。生成模型可提供更快速的样本生成，但目前需要耗费大量精力针对特定探测器几何结构优化性能，通常需要许多网络来描述变化的单元尺寸和排列方式，且无法泛化至其他几何结构。我们开发了一种**几何感知**自回归模型，该模型学习量热器响应如何随几何结构变化，并能够在无需额外训练的情况下生成针对未见几何结构的模拟响应。该几何感知模型在关键量的生成分布与真实分布之间的瓦瑟斯坦距离等指标上，比基准无感知模型表现提升50%。这些关键量可总结模拟响应特征。一个单一的几何感知模型可取代目前在大型强子对撞机数据分析中物理学家为量热器模拟设计的数百个生成模型。对于未来探测器的研究而言，这种基础模型将成为关键工具，可大幅减少开发生成式量热器模型通常所需的前期投入。