Improving the predictive power of empirical shell-model Hamiltonians

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.