Statistical validation of calorimeter inpainting with generative diffusion priors

Himanshu Raj; Roli Esha

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arXiv · 2608.14801

Statistical validation of calorimeter inpainting with generative diffusion priors

Himanshu Raj·Roli Esha

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Abstract

Localized detector inefficiencies produce incomplete calorimeter data that limit the ability to perform precision measurements. We address this problem in relativistic heavy-ion collisions from a Bayesian perspective using pretrained calorimeter diffusion models as priors to reconstruct the missing signal conditioned on surrounding measurements. In this work, we conduct a systematic comparison of several diffusion-based inpainting algorithms, whose performance is evaluated using Bayesian posterior diagnostics of energy response, spatial bias, and uncertainty calibration. The reconstruction fidelity is also analyzed across collision centralities and masked region sizes. This study establishes a general validation strategy for probabilistic reconstruction of missing detector information.

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Himanshu Raj, Roli Esha. 2026-08-14. Statistical validation of calorimeter inpainting with generative diffusion priors. https://arxiv.org/abs/2608.14801

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