Hypernuclei with Neural Network Quantum States

TL;DR

This paper introduces a neural network quantum state approach to compute properties of hypernuclei, achieving good agreement with experiments and extending the method to include strange particles, paving the way for advanced nuclear modeling.

Contribution

It develops a neural network variational Monte Carlo method for hypernuclei, incorporating $ ext{Lambda}$ particles and improved effective field theory interactions.

Findings

Predicted binding energies match experimental data.

Confirmed proton radius shrinkage in $^7_\Lambda$Li.

Extended neural network quantum states to hypernuclei.

Abstract

Leveraging complementary machine-learning-based approaches, we compute properties of $s$- and $p$-shell $\Lambda$ hypernuclei - including binding energies, single-particle densities, and radii - starting from the individual interactions among their constituents. These interactions are modeled using an improved leading-order pionless effective field theory expansion, with coefficients determined via a Gaussian Process framework anchored on virtually exact few-body techniques. We solve the many-body Schr\"odinger equation using a variational Monte Carlo method based on neural network quantum states, extending it for the first time to include $\Lambda$ particles alongside protons and neutrons. The predicted binding energies show remarkably good agreement with experimental results, given the simplicity of the input Hamiltonian. We also confirm the experimentally observed shrinkage of the proton radius in $^7_\Lambda$Li compared to its parent nucleus, $^6$Li. This work paves the way for an ab initio description of medium-mass and heavy hypernuclei, as well as for understanding the onset of strange degrees of freedom in the core of neutron stars.