Statistical validation of calorimeter inpainting with generative diffusion priors

[2608.14801] Statistical validation of calorimeter inpainting with generative diffusion priors Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Physics > Data Analysis, Statistics and Probability arXiv:2608.14801 (physics) [Submitted on 14 Aug 2026] Title: Statistical validation of calorimeter inpainting with generative diffusion priors Authors: Himanshu Raj , Roli Esha View a PDF of the paper titled Statistical validation of calorimeter inpainting with generative diffusion priors, by Himanshu Raj and 1 other authors View PDF HTML (experimental) 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. Comments: 21 pages, 15 figures Subjects: Data Analysis, Statistics and Probability (physics.data-an) ; High Energy Physics - Experiment (hep-ex); Nuclear Experiment (nucl-ex) Cite as: arXiv:2608.14801 [physics.data-an] (or arXiv:2608.14801v1 [physics.data-an] for this version) https://doi.org/10.48550/arXiv.2608.14801 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Roli Esha [ view email ] [v1] Fri, 14 Aug 2026 18:14:23 UTC (1,293 KB) Full-text links: Access Paper: View a PDF of the paper titled Statistical validation of calorimeter inpainting with generative diffusion priors, by Himanshu Raj and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: physics.data-an < prev | next > new | recent | 2026-08 Change to browse by: hep-ex nucl-ex physics References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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