High Precision Binding Energies from Physics Informed Machine Learning
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909532854157312 |
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| author | Bentley, Ian Tedder, James Gebran, Marwan Paul, Ayan |
| author_facet | Bentley, Ian Tedder, James Gebran, Marwan Paul, Ayan |
| contents | Twelve physics-informed machine learning models have been trained to model binding energy residuals. Our approach begins with determining the difference between measured experimental binding energies and three different mass models. Then four machine learning approaches are used to train on each energy difference. The most successful ML technique, both in interpolation and extrapolation, is the least squares boosted ensemble of trees. The best model resulting from that technique utilizes eight physical features to model the difference between experimental atomic binding energy values in AME 2012 and the Duflo Zuker mass model. This resulted in a model that fit the training data with a standard deviation of 17 keV and that has a standard deviation of 92 keV when compared all of the values in the AME 2020. The extrapolation capability of each model is discussed, and the accuracy of predicting new mass measurements has also been tested. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_09504 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | High Precision Binding Energies from Physics Informed Machine Learning Bentley, Ian Tedder, James Gebran, Marwan Paul, Ayan Nuclear Theory Computational Physics Twelve physics-informed machine learning models have been trained to model binding energy residuals. Our approach begins with determining the difference between measured experimental binding energies and three different mass models. Then four machine learning approaches are used to train on each energy difference. The most successful ML technique, both in interpolation and extrapolation, is the least squares boosted ensemble of trees. The best model resulting from that technique utilizes eight physical features to model the difference between experimental atomic binding energy values in AME 2012 and the Duflo Zuker mass model. This resulted in a model that fit the training data with a standard deviation of 17 keV and that has a standard deviation of 92 keV when compared all of the values in the AME 2020. The extrapolation capability of each model is discussed, and the accuracy of predicting new mass measurements has also been tested. |
| title | High Precision Binding Energies from Physics Informed Machine Learning |
| topic | Nuclear Theory Computational Physics |
| url | https://arxiv.org/abs/2412.09504 |