MCNP-Simulated HPGe Soil Spectra: A Public Dataset and Machine Learning Benchmark for Multi-Isotope Quantification

MCNP-Simulated HPGe Soil Spectra: A Public Dataset and Machine Learning Benchmark for Multi-Isotope Quantification | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article MCNP-Simulated HPGe Soil Spectra: A Public Dataset and Machine Learning Benchmark for Multi-Isotope Quantification An Trung Nguyen, Nhu Hai Phung, Chi Thanh Nguyen, Hao Quang Nguyen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6532814/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Nov, 2025 Read the published version in International Journal of Data Science and Analytics → Version 1 posted 9 You are reading this latest preprint version Abstract Accurate identification and quantification of radionuclides in soil are essential for environmental monitoring, nuclear safety, and emergency response. Machinelearning methods operating on fullspectrum gammaray data offer a promising alternative to traditional peakbased analysis, but their development is impeded by the lack of large, publicly accessible training datasets that reflect realistic soil matrices and highresolution HPGe (High-Purity Germanium) detector characteristics. In this work, we introduce the first open Monte Carlo N-Particle transport (MCNP)based HPGe soil gammaray spectroscopy dataset, comprising 6000 simulated spectra covering 41 prevalent radionuclides across diverse activity levels. We benchmark four regression approaches — Ridge Regression, Extreme Gradient Boosting Regression, Multilayer Perceptron, and Convolutional Neural Network — on quantification tasks using a held-out test set. Linear and ensemble methods achieve robust baselines, successfully predicting over 95% of isotopes within ± 15% relative error, whereas the tested deeplearning architectures exhibit greater variability on lowintensity and overlappingpeak nuclides. These results demonstrate the dataset’s utility for reproducible research and highlight significant opportunities for architectural innovations and domainadaptation strategies to enhance deeplearning performance. We anticipate that this resource will catalyze the development of more accurate, generalizable machinelearning solutions for multiisotope activity quantification in environmental applications. MCNP Radioisotope Quantification Machine Learning Gamma Spectra Deep Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Nov, 2025 Read the published version in International Journal of Data Science and Analytics → Version 1 posted Editorial decision: Revision requested 24 Jun, 2025 Reviews received at journal 15 Jun, 2025 Reviews received at journal 11 Jun, 2025 Reviewers agreed at journal 11 Jun, 2025 Reviewers agreed at journal 09 Jun, 2025 Reviewers invited by journal 08 Jun, 2025 Editor assigned by journal 09 May, 2025 Submission checks completed at journal 26 Apr, 2025 First submitted to journal 26 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6532814","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":468770549,"identity":"21305c96-d0c6-4843-af18-a65680363d4f","order_by":0,"name":"An Trung Nguyen","email":"","orcid":"","institution":"Viet Nam Atomic Energy Institute","correspondingAuthor":false,"prefix":"","firstName":"An","middleName":"Trung","lastName":"Nguyen","suffix":""},{"id":468770550,"identity":"e1b6b405-fae6-4cc0-8133-54f78529cd46","order_by":1,"name":"Nhu Hai 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Machinelearning methods operating on fullspectrum gammaray data offer a promising alternative to traditional peakbased analysis, but their development is impeded by the lack of large, publicly accessible training datasets that reflect realistic soil matrices and highresolution HPGe (High-Purity Germanium) detector characteristics. In this work, we introduce the first open Monte Carlo N-Particle transport (MCNP)based HPGe soil gammaray spectroscopy dataset, comprising 6000 simulated spectra covering 41 prevalent radionuclides across diverse activity levels. We benchmark four regression approaches \u0026mdash; Ridge Regression, Extreme Gradient Boosting Regression, Multilayer Perceptron, and Convolutional Neural Network \u0026mdash; on quantification tasks using a held-out test set. 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