• Open Access

Dilute neutron star matter from neural-network quantum states

Bryce Fore, Jane M. Kim, Giuseppe Carleo, Morten Hjorth-Jensen, Alessandro Lovato, and Maria Piarulli
Phys. Rev. Research 5, 033062 – Published 31 July 2023

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 of pairing in the 1S0 channel.

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  • Received 14 December 2022
  • Accepted 9 July 2023

DOI:https://doi.org/10.1103/PhysRevResearch.5.033062

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Published by the American Physical Society

Physics Subject Headings (PhySH)

Nuclear Physics

Authors & Affiliations

Bryce Fore1, Jane M. Kim2, Giuseppe Carleo3, Morten Hjorth-Jensen2,4, Alessandro Lovato1,5,6, and Maria Piarulli7,8

  • 1Physics Division, Argonne National Laboratory, Argonne, Illinois 60439, USA
  • 2Department of Physics and Astronomy and Facility for Rare Isotope Beams, Michigan State University, East Lansing, Michigan 48824, USA
  • 3Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
  • 4Department of Physics and Center for Computing in Science Education, University of Oslo, N-0316 Oslo, Norway
  • 5Computational Science Division, Argonne National Laboratory, Argonne, Illinois 60439, USA
  • 6INFN-TIFPA Trento Institute for Fundamental Physics and Applications, 38123 Trento, Italy
  • 7Physics Department, Washington University, St Louis, Missouri 63130, USA
  • 8McDonnell Center for the Space Sciences at Washington University in St. Louis, Missouri 63130, USA

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Vol. 5, Iss. 3 — July - September 2023

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