Hyperon-Nucleon Interaction Constrained by Light Hypernuclei

TL;DR

This paper develops a hyperon-nucleon interaction model constrained by hypernuclear data, improving predictions of hypernuclear properties and employing machine learning for uncertainty quantification.

Contribution

It introduces a hyperon-nucleon interaction constrained by experimental hypernuclear data, enhancing the accuracy of hypernuclear structure calculations.

Findings

Remedied previous overestimation of hyperon separation energies.

Improved description of the $_\Lambda$He isotopic chain.

Applied machine learning for uncertainty quantification.

Abstract

Ab initio structure calculations for p-shell hypernuclei have recently become accessible through extensions of nuclear many-body methods, such as the no-core shell model, in combination with hyperon-nucleon interactions from chiral effective field theory. However, the low-energy constants in these hyperon-nucleon interactions are poorly constraint due to the very limited amount of experimental scattering data available. We present a hyperon-nucleon interaction that is additionally constrained by experimental ground-state and spectroscopic data for selected p-shell hypernuclei and, thus, optimized for hypernuclear structure calculations. We show that the previous overestimation of the hyperon separation energies in the p-shell is remedied and discuss the significantly improved description of the $_\Lambda$He isotopic chain. We further discuss the uncertainty quantification for hypernuclear observables on the many-body level, obtained through a novel machine-learning tool.