Improving the predictive power of empirical shell-model Hamiltonians

TL;DR

This paper introduces two methods to improve the predictive accuracy of empirical shell-model Hamiltonians, especially in data-sparse scenarios, by refining the starting point and preventing overfitting.

Contribution

It presents a new approach combining ab initio derived Hamiltonians with a protocol to avoid overfitting, enhancing extrapolation reliability.

Findings

Better predictions for exotic isotopes.

Enhanced robustness in sparse data conditions.

Improved extrapolation beyond available data.

Abstract

We present two developments which enhance the predictive power of empirical shell-model Hamiltonians for cases in which calibration data are sparse. A recent improvement in the ab initio derivation of effective Hamiltonians leads to a much better starting point for the optimization procedure. In addition, we introduce a protocol to avoid overfitting, enabling a more reliable extrapolation beyond available data. These developments will enable more robust predictions for exotic isotopes produced at rare isotope beam facilities and in astrophysical environments.