dorsal/arxiv
View SchemaDifferentiable Surrogate for Detector Simulation and Design with Diffusion Models
| Authors | Xuan Tung Nguyen, Long Chen, Tommaso Dorigo, Nicolas R. Gauger, Pietro Vischia, Federico Nardi, Muhammad Awais, Hamza Hanif, Shahzaib Abbas, Rukshak Kapoor |
|---|---|
| Categories | |
| ArXiv ID | 2601.07859vv1 |
| URL | https://arxiv.org/abs/2601.07859 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
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.
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"abstract": "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.",
"arxiv_id": "2601.07859",
"authors": [
"Xuan Tung Nguyen",
"Long Chen",
"Tommaso Dorigo",
"Nicolas R. Gauger",
"Pietro Vischia",
"Federico Nardi",
"Muhammad Awais",
"Hamza Hanif",
"Shahzaib Abbas",
"Rukshak Kapoor"
],
"categories": [
"physics.ins-det",
"hep-ex"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "Differentiable Surrogate for Detector Simulation and Design with Diffusion Models",
"url": "https://arxiv.org/abs/2601.07859",
"version": "v1"
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