Predicting (n,3n) nuclear reaction cross-sections using XGBoost and Leave-One-Out Cross-Validation

Accurately predicting nuclear reaction cross-sections is crucial for advancing various fields, including nuclear medicine, energy production, and materials science. This study aims to address the challenges associated with predicting (n ,3n) nuclear reaction cross-sections by developing a robust mac...

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Veröffentlicht in:Applied radiation and isotopes Jg. 219; S. 111714
Hauptverfasser: Ali Üncü, Yiğit, Danışman, Taner, Özdoğan, Hasan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Elsevier Ltd 01.05.2025
Schlagworte:
(n,3n) reactions
Cross-section
Leave-One-Out Cross-Validation
Machine learning
TALYS 1.95
TENDL
XGBoost
TENDL
XGBoost
Cross-section
Machine learning
TALYS 1.95
Leave-One-Out Cross-Validation
(n,3n) reactions
ISSN:0969-8043, 1872-9800, 1872-9800
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Zusammenfassung:Accurately predicting nuclear reaction cross-sections is crucial for advancing various fields, including nuclear medicine, energy production, and materials science. This study aims to address the challenges associated with predicting (n ,3n) nuclear reaction cross-sections by developing a robust machine learning (ML) model based on the XGBoost (eXtreme Gradient Boosting) algorithm. By leveraging a comprehensive dataset of experimental cross-sectional values, the study demonstrates the potential of ML to overcome limitations in existing theoretical and empirical approaches. LOOCV (Leave-One-Out Cross-Validation) was employed for feature selection and hyperparameter optimization to ensure the reliability of the model. The dataset was meticulously prepared by normalizing values and addressing missing data, which contributed to robust model training. XGBoost's ability to handle complex, non-linear relationships enabled it to provide accurate predictions that closely align with experimental data, as evaluated through key metrics such as Mean Squared Error (MSE) and Mean Absolute Error (MAE), and reduced Chi-Square. To validate the model's accuracy, its predictions were compared with calculations from the TALYS 1.95 nuclear reaction code, TENDL and phenological model. The results highlight the efficacy of XGBoost in improving prediction accuracy, offering a novel approach to solving complex challenges in nuclear data analysis. •Accurate machine learning algorithms have been developed to estimate (n,3n) reaction cross-section.•XGBoost algorithm is found the best option for (n,3n) reaction cross-section for classification algorithms.•To compare the XGBoost estimations, reaction cross-section calculations have been done by using TALYS 1.95 code.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0969-8043
1872-9800
1872-9800
DOI:10.1016/j.apradiso.2025.111714