End-to-End Detector Optimization with Diffusion Models: A Case Study in Sampling Calorimeters

Recent advances in machine learning have opened new avenues for optimizing
detector designs in high-energy physics, where the complex interplay of geometry, materi-
als, and physics processes has traditionally posed a significant challenge. In this work, we
introduce the end-to-end. AI Detector Optimization framework (AIDO), which leverages
a diffusion model as a surrogate for the full simulation and reconstruction chain, enabling
gradient-based design exploration in both continuous and discrete parameter spaces. Al-
though this framework is applicable to a broad range of detectors, we illustrate its power
using the specific example of a sampling calorimeter, focusing on charged pions and photons
as representative incident particles. Our results demonstrate that the diffusion model effec-
tively captures critical performance metrics for calorimeter design, guiding the automatic
search for a layer arrangement and material composition that align with known calorimeter
principles. The success of this proof-of-concept study provides a foundation for the future ap-
plications of end-to-end optimization to more complex detector systems, offering a promising
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path toward systematically exploring the vast design space in next-generation experiments.