Dilute neutron star matter from neural-network quantum states

TL;DR

This paper introduces a neural-network quantum state approach to model low-density neutron matter, capturing superfluid phenomena and pairing with reduced computational cost compared to traditional methods.

Contribution

It demonstrates that neural-network quantum states combined with variational techniques can effectively simulate neutron matter, offering a computationally efficient alternative to existing methods.

Findings

Neural-network approach achieves competitive energy calculations

Emergence of pairing in the $^1S_0$ channel observed

Method reduces computational cost compared to auxiliary-field diffusion Monte Carlo

Abstract

Low-density neutron matter is characterized by fascinating emergent quantum phenomena, such as the formation of Cooper pairs and the onset of superfluidity. We model this density regime by capitalizing on the expressivity of the hidden-nucleon neural-network quantum states combined with variational Monte Carlo and stochastic reconfiguration techniques. Our approach is competitive with the auxiliary-field diffusion Monte Carlo method at a fraction of the computational cost. Using a leading-order pionless effective field theory Hamiltonian, we compute the energy per particle of infinite neutron matter and compare it with those obtained from highly realistic interactions. In addition, a comparison between the spin-singlet and triplet two-body distribution functions indicates the emergence pairing in the $^1S_0$ channel.