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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| Vydáno v: | Applied radiation and isotopes Ročník 219; s. 111714 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.05.2025
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| ISSN: | 0969-8043, 1872-9800, 1872-9800 |
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| Abstract | 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. |
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| AbstractList | 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.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. 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. 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. |
| ArticleNumber | 111714 |
| Author | Ali Üncü, Yiğit Özdoğan, Hasan Danışman, Taner |
| Author_xml | – sequence: 1 givenname: Yiğit surname: Ali Üncü fullname: Ali Üncü, Yiğit organization: Akdeniz University, Vocational School of Technical Sciences, Department of Biomedical Equipment Technology, 07070, Antalya, Turkey – sequence: 2 givenname: Taner surname: Danışman fullname: Danışman, Taner organization: Akdeniz University, Faculty of Engineering, Department of Computer Engineering, 07070, Antalya, Turkey – sequence: 3 givenname: Hasan orcidid: 0000-0001-6127-9680 surname: Özdoğan fullname: Özdoğan, Hasan email: hasan.ozdogan@antalya.edu.tr organization: Antalya Bilim University, Vocational School of Health Services, Department of Medical Imaging Techniques, 07140, Antalya, Turkey |
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| Keywords | TENDL XGBoost Cross-section Machine learning TALYS 1.95 Leave-One-Out Cross-Validation (n,3n) reactions |
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