Orateurs
Bhoomika Maheshwari
(GANIL)M.
Pieter Van Isacker
(GANIL)
Description
We present a hybrid machine-learning framework that combines high-accuracy numerical regression with symbolic regression to model and interpret nuclear charge radii. Using Light Gradient Boosting and Gaussian Process Regression with rigorous cross-validation, the method reproduces experimental trends across the nuclear chart and distills them into simple analytical expressions. These formulas naturally recover liquid-drop–like dependencies and reveal new correlations from pairing and binding energies, demonstrating data-driven discovery of physical laws in nuclear structure.
Auteurs
Bhoomika Maheshwari
(GANIL)
M.
Pieter Van Isacker
(GANIL)
