Generalizing to new geometries with Geometry-Aware Autoregressive Models (GAAMs) for fast calorimeter simulation

Understand

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.