Differentiable Surrogate for Detector Simulation and Design with Diffusion Models
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917415077543936 |
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| author | Nguyen, Xuan Tung Chen, Long Dorigo, Tommaso Gauger, Nicolas R. Vischia, Pietro Nardi, Federico Awais, Muhammad Hanif, Hamza Abbas, Shahzaib Kapoor, Rukshak |
| author_facet | Nguyen, Xuan Tung Chen, Long Dorigo, Tommaso Gauger, Nicolas R. Vischia, Pietro Nardi, Federico Awais, Muhammad Hanif, Hamza Abbas, Shahzaib Kapoor, Rukshak |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_07859 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Differentiable Surrogate for Detector Simulation and Design with Diffusion Models Nguyen, Xuan Tung Chen, Long Dorigo, Tommaso Gauger, Nicolas R. Vischia, Pietro Nardi, Federico Awais, Muhammad Hanif, Hamza Abbas, Shahzaib Kapoor, Rukshak Instrumentation and Detectors High Energy Physics - Experiment 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. |
| title | Differentiable Surrogate for Detector Simulation and Design with Diffusion Models |
| topic | Instrumentation and Detectors High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2601.07859 |