SciPost Submission Page
Predicting Nuclear Binding Energy Using Generalized Additive Model
by Kristiyan Laoli
Submission summary
| Authors (as registered SciPost users): | Kristiyan Laoli |
| Submission information | |
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| Preprint Link: | scipost_202410_00030v1 (pdf) |
| Code repository: | https://github.com/kristiyanlaoli/Predicting-Nuclear-Binding-Energy-Using-Generalized-Additive-Model |
| Data repository: | http://dx.doi.org/10.13140/RG.2.2.20565.03041 |
| Date submitted: | 2024-10-14 13:41 |
| Submitted by: | Laoli, Kristiyan |
| Submitted to: | SciPost Physics |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approach: | Computational |
Abstract
This study presents the implementation of the Generalized Additive Model (GAM) for predicting nuclear binding energies. GAM, a non-parametric regression model, effectively captures complex, non-linear relationships between input variables and the output, providing an interpretable framework for understanding the contribution of each nuclear property to the binding energy. The model’s performance is evaluated using data from the Atomic Mass Evaluation (AME) 2020, yielding promising results with a Root Mean Square Error (RMSE) of 0.3 MeV. This study demonstrates the potential of machine learning methods, such as GAM, in nuclear physics, particularly for complex, many-body problems where traditional methods face computational challenges.
Author indications on fulfilling journal expectations
- Provide a novel and synergetic link between different research areas.
- Open a new pathway in an existing or a new research direction, with clear potential for multi-pronged follow-up work
- Detail a groundbreaking theoretical/experimental/computational discovery
- Present a breakthrough on a previously-identified and long-standing research stumbling block
Current status:
In refereeing