Performance of various kernel functions for mass prediction with support vector machine

Document Type

Article

Publication Date

6-1-2025

Abstract

This manuscript explores the potential of Support Vector Machines (SVM) in predicting nuclear binding energies, emphasizing the critical role of kernel functions in improving model accuracy. We systematically assess the performance of various SVM kernel functions-linear, polynomial, radial basis function, and sigmoid-through rigorous hyperparameter optimization and cross-validation. The study demonstrates that the radial basis function kernel outperforms other kernels, achieving the lowest root-mean-square deviation of 0.199 MeV, making it the most effective for nuclear mass predictions. By integrating key nuclear physics features, including mass model information, the SVM model is able to capture complex nuclear behaviors across different mass ranges. We present a comprehensive comparison of our SVM model against conventional mass models such as liquid drop model and WS4, where our SVM model shows improved predictive accuracy. This work underscores the significance of kernel selection in SVM and highlights the power of machine learning in advancing nuclear mass spectroscopy, providing a valuable framework for future computational modeling in nuclear physics.

Publication Source (Journal or Book title)

European Physical Journal A

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