aiqbench analysis
This study applies Kolmogorov-Arnold Networks (KAN) to refine nuclear mass models, demonstrating how interpretable machine learning can extract physical insights from small-sample scientific datasets. By reducing prediction errors and identifying systematic biases in proton-related terms, the research highlights a viable pathway for data-driven discovery in high-complexity physics problems.
Bridging Theory and Data: Correcting Nuclear Mass Models with Interpretable Machine Learning
arXiv:2603.15203v1 Announce Type: cross Abstract: Nuclear mass prediction is one of the core issues in nuclear physics research, yet it faces the challenge of small-sample datasets with high complexity. This study introduces the Kolmogorov-Arnold Network (KAN) into the refinement of nuclear mass models, proposing an efficient and interpretable solution. By constructing the KAN-WS4 hybrid model, the prediction accuracy is significantly improved (the root mean square error is reduced from 0.3 MeV to 0.16 MeV). Furthermore, leveraging the intrinsic interpretability of KAN, feature importance analysis reveals that the proton number is the most critical factor influencing residuals, indicating potential systematic biases in proton-related terms within existing theoretical models. The method's generality is demonstrated across five mass models. This study shows that KAN provides a novel approach to small-sample, high-complexity scientific problems. Its interpretability facilitates the data-driven discovery of physical laws, promising broad applicability to key nuclear physics issues.
Abstract:Nuclear mass prediction is one of the core issues in nuclear physics research, yet it faces the challenge of small-sample datasets with high complexity. This study introduces the Kolmogorov-Arnold Network (KAN) into the refinement of nuclear mass models, proposing an efficient and interpretable solution. By constructing the KAN-WS4 hybrid model, the prediction accuracy is significantly improved (the root mean square error is reduced from 0.3 MeV to 0.16 MeV). Furthermore, leveraging the intrinsic interpretability of KAN, feature importance analysis reveals that the proton number is the most critical factor influencing residuals, indicating potential systematic biases in proton-related terms within existing theoretical models. The method's generality is demonstrated across five mass models. This study shows that KAN provides a novel approach to small-sample, high-complexity scientific problems. Its interpretability facilitates the data-driven discovery of physical laws, promising broad applicability to key nuclear physics issues.
Nuclear Theory (nucl-th); Machine Learning (cs.LG); Nuclear Experiment (nucl-ex)
arXiv:2603.15203 [nucl-th]
(or arXiv:2603.15203v1 [nucl-th] for this version)
https://doi.org/10.48550/arXiv.2603.15203 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 240 words
