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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.11066 |
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| _version_ | 1866914337130545152 |
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| author | Bentley, I. Tedder, J. Gebran, M. Paul, A. |
| author_facet | Bentley, I. Tedder, J. Gebran, M. Paul, A. |
| contents | This paper describes the development of the Four Model Tree Ensemble (FMTE). The FMTE is a composite of machine learning models trained on experimental binding energies from the Atomic Mass Evaluation (AME) 2012. The FMTE predicts binding energy values for all nuclei with N > 7 and Z > 7 from AME 2020 with a standard deviation of 76 keV and a mean average deviation of 34 keV. The FMTE model was developed by combining three new models with one prior model. The new models presented here have been trained on binding energy residuals from mass models using four machine learning approaches. The models presented in this work leverage shape parameters along with other physical features. We have determined the preferred machine learning approach for binding energy residuals is the least-squares boosted ensemble of trees. This approach appears to have a superior ability to both interpolate and extrapolate binding energy residuals. A comparison with the masses of isotopes that were not measured previously and a discussion of extrapolations approaching the neutron drip line have been included. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11066 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Further exploration of binding energy residuals using machine learning and the development of a composite ensemble model Bentley, I. Tedder, J. Gebran, M. Paul, A. Nuclear Theory Machine Learning This paper describes the development of the Four Model Tree Ensemble (FMTE). The FMTE is a composite of machine learning models trained on experimental binding energies from the Atomic Mass Evaluation (AME) 2012. The FMTE predicts binding energy values for all nuclei with N > 7 and Z > 7 from AME 2020 with a standard deviation of 76 keV and a mean average deviation of 34 keV. The FMTE model was developed by combining three new models with one prior model. The new models presented here have been trained on binding energy residuals from mass models using four machine learning approaches. The models presented in this work leverage shape parameters along with other physical features. We have determined the preferred machine learning approach for binding energy residuals is the least-squares boosted ensemble of trees. This approach appears to have a superior ability to both interpolate and extrapolate binding energy residuals. A comparison with the masses of isotopes that were not measured previously and a discussion of extrapolations approaching the neutron drip line have been included. |
| title | Further exploration of binding energy residuals using machine learning and the development of a composite ensemble model |
| topic | Nuclear Theory Machine Learning |
| url | https://arxiv.org/abs/2503.11066 |