翻訳待ち：Learning to Resolve Neutron Resonances with Fully Convolutional Neural Networks
AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.04027v1 Announce Type: new Abstract: This work investigates the feasibility of augmenting traditional R-Matrix codes with a robust machine learning framework for automatically detecting neutron resonances in transmission spectra. Neutron transmission data are often complex and noisy, making them difficult to analyze using traditional peak-identification methods. The state-of-the-art R-Matrix codes currently used by physicists to fit these data often depend on prior evaluations and require substantial manual effort. This preliminary study demonstrates a method for accelerating the post-experimental processing of neutron transmission data and reducing bias associated with dependence on prior evaluations. We employ a fully convolutional neural network to classify individual points as belonging to resonance or non-resonance regions in seven transmission spectra---two evaluated and five experimental. Although the model achieves classification accuracies in the range of 93\%, further analysis shows that this metric overstates its ability to generalize. Building on our prior analysis in PHYSOR 2026, we find that, despite the inclusion of additional training data, the method does not generalize reliably to previously unseen isotopes. To address these limitations, future work should evaluate whether a larger and more diverse training dataset can produce a generalizable model and should incorporate known physical characteristics of neutron resonances to improve model performance.
AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。
--> [Submitted on 27 Jul 2026] Title:Learning to Resolve Neutron Resonances with Fully Convolutional Neural Networks View a PDF of the paper titled Learning to Resolve Neutron Resonances with Fully Convolutional Neural Networks, by Nataly R. Panczyk and 3 other authors View PDF Abstract:This work investigates the feasibility of augmenting traditional R-Matrix codes with a robust machine learning framework for automatically detecting neutron resonances in transmission spectra. Neutron transmission data are often complex and noisy, making them difficult to analyze using traditional peak-identification methods. The state-of-the-art R-Matrix codes currently used by physicists to fit these data often depend on prior evaluations and require substantial manual effort. This preliminary study demonstrates a method for accelerating the post-experimental processing of neutron transmission data and reducing bias associated with dependence on prior evaluations. We employ a fully convolutional neural network to classify individual points as belonging to resonance or non-resonance regions in seven transmission spectra---two evaluated and five experimental. Although the model achieves classification accuracies in the range of 93\%, further analysis shows that this metric overstates its ability to generalize. Building on our prior analysis in PHYSOR 2026, we find that, despite the inclusion of additional training data, the method does not generalize reliably to previously unseen isotopes. To address these limitations, future work should evaluate whether a larger and more diverse training dataset can produce a generalizable model and should incorporate known physical characteristics of neutron resonances to improve model performance. Comments: 29 pages, 11 figures, and 4 tables Subjects: Machine Learning (cs.LG); Applied Physics (physics.app-ph) Cite as: arXiv:2608.04027 [cs.LG] (or arXiv:2608.04027v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.04027 arXiv-issued DOI via DataCite Submission history From: Majdi Radaideh [view email] [v1] Mon, 27 Jul 2026 18:43:20 UTC (3,147 KB) Full-text links: Access Paper: View a PDF of the paper titled Learning to Resolve Neutron Resonances with Fully Convolutional Neural Networks, by Nataly R. Panczyk and 3 other authors View PDF TeX Source view license Current browse context: cs.LG new | recent | 2026-08 Change to browse by: cs physics physics.app-ph References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: 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?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) 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?)