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. This proof-of-concept study motivates the design of a foundational model that will be a crucial tool for the study of future detectors, dramatically reducing the large upfront investment usually needed to develop generative calorimeter models.

翻译：生成碰撞产物经过探测器后的模拟响应，是粒子物理数据分析的关键环节，但其计算成本极高。其中，量热仪子探测器因其单元的高粒度和相互作用的复杂性，占据主导计算耗时。生成模型虽能实现更快速的样本生成，但目前需耗费大量精力针对特定探测器几何结构优化性能，且往往需要多个模型来描述不同的单元尺寸与排列方式，缺乏泛化至其他几何结构的能力。我们开发了一种几何感知自回归模型，该模型能够学习量热仪响应随几何结构变化的规律，并可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。在多项指标（如生成数据与真实数据之间关于关键汇总量的Wasserstein距离）上，该几何感知模型相较于基线无感知模型性能提升超过50%。单个几何感知模型即可取代当前物理学家为分析大型强子对撞机数据而设计的数百个量热仪模拟生成模型。这项概念验证研究为设计基础模型提供了动机，该模型将成为未来探测器研究的关键工具，并显著减少开发生成式量热仪模型通常所需的大量前期投入。