Nuclear Theory·nucl-th
4 papers—2 primary·2 cross-listed
[Submitted on 14 Jan 2025]
Cong Li🇨🇳 · Yutong Xiao🇨🇳
We studied the influence of background fields on coupling constants in various kinematic regions. We then evaluated these effects through the Bethe-Heitler process in electron-nucleus collisions at the EIC and EicC.
[Submitted on 15 Jan 2025]
Marco Knöll · Marc L. Agel · Tobias Wolfgruber · Pieter Maris · Robert Roth
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.
first authorsco-authorsvia INSPIRE