2025/01/15 by Marco Knöll, Knöll, Marco, Marc L. Agel +7
Computer Science · Engineering · #FOS: Physical sciences #Non-Destructive Testing Techniques #Nuclear Theory (nucl-th) #Sensor Technology and Measurement Systems #Welding Techniques and Residual Stresses
paper · pdf · doi:10.48550/arxiv.2501.08959
openalex publication_date 2025/01/15 · openalex created_date 2025/01/17 · openalex updated_date 2026/08/03
In ab initio nuclear structure theory, accurately predicting electromagnetic observables, such as moments and transition rates, is essential for a comprehensive understanding of nuclear properties. However, computational limitations and conceptual difficulties often hinder the precise calculation of these observables. In this work, we extend machine learning methods for model-space extrapolations to electric quadrupole moments. We further present a new machine learning approach that leverages the correlations between energies, radii, and electromagnetic observables. By learning these correlations from no-core shell model calculations in accessible model spaces, this new model enables the prediction of converged electromagnetic observables from predictions of converged energies and radii, which can be obtained with established machine learning extrapolation tools. An essential property of our approach is the capability for uncertainty quantification, allowing for reliable predictions with combined statistical error estimates for many-body and interaction uncertainties. Being solely built upon the physical correlations of different observables, it can be generalized across different ab initio methods. We demonstrate the power of this new extrapolation scheme through a precision study of electric quadrupole moments across a wide range of p-shell nuclei.