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Differentiable surrogate for detector simulation and design with diffusion models
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DOI：10.1088/2632-2153/ae5c56.png)
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Abstract
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In this work, we present a conditional denoising-diffusion surrogate for electromagnetic calorimeter showers that is trained to generate high-fidelity energy-deposition maps conditioned on key detector and beam parameters. The model employs efficient inference using denoising diffusion implicit model sampling and is pre-trained on GEANT4 simulations before being adapted to a new calorimeter geometry through low-rank adaptation, requiring only a small post-training dataset. We evaluate physically meaningful observables, including total deposited energy, energy-weighted radius, and shower dispersion, obtaining relative root mean square error values below 2 for representative high-energy cases. This is in line with state-of-the-art calorimeter surrogates which report comparable fidelity on high-level observables. Furthermore, we compare gradients of a reconstruction-based utility function with respect to design parameters between the surrogate and finite-difference references. The diffusion surrogate reproduces the qualitative structure and directional trends of the true utility landscape, providing usable sensitivities for gradient-based optimization. These results show that diffusion-based surrogates can accelerate simulation-driven detector design while enabling differentiable, gradient-informed analysis.
Keywords:
diffusion models
detector simulation
surrogate modeling
gradient-based optimization
calorimeter showers
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