Statistical validation of calorimeter inpainting with generative diffusion priors
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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BibTeX
@misc{raj2026statistical,
title = {{Statistical validation of calorimeter inpainting with generative diffusion priors}},
author = {Himanshu Raj and Roli Esha},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2608.14801},
eprint = {2608.14801},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.14801},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Statistical validation of calorimeter inpainting with generative diffusion priors
AU - Himanshu Raj
AU - Roli Esha
PY - 2026
DA - 2026/09/29
PB - arXiv
DO - 10.48550/arxiv.2608.14801
UR - https://arxiv.org/abs/2608.14801
AB - 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.
ER -