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Nuclei with Up to \(\varvec{A=6}\) Nucleons with Artificial Neural Network Wave Functions

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Abstract

The ground-breaking works of Weinberg have opened the way to calculations of atomic nuclei that are based on systematically improvable Hamiltonians. Solving the associated many-body Schrödinger equation involves non-trivial difficulties, due to the non-perturbative nature and strong spin-isospin dependence of nuclear interactions. Artificial neural networks have proven to be able to compactly represent the wave functions of nuclei with up to \(A=4\) nucleons. In this work, we extend this approach to \(^6\)Li and \(^6\)He nuclei, using as input a leading-order pionless effective field theory Hamiltonian. We successfully benchmark their binding energies, point-nucleon densities, and radii with the highly-accurate hyperspherical harmonics method.

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Acknowledgements

The present research is supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics, under contracts DE-AC05-06OR23177 (A.G.), DE-AC02-06CH11357, by the NUCLEI SciDAC program (A.L., N.B.) and by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics (N.R.). A.L. and N.B. were also supported by DOE Early Career Research Program and Argonne LDRD awards. A.L acknowledges funding from the INFN grant INNN3, and from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 824093. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. The calculations were performed using resources of the Laboratory Computing Resource Center of Argonne National Laboratory, the National Energy Research Supercomputer Center (NERSC), and through a CINECA-INFN agreement that provides access to resources on MARCONI at CINECA.

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Authors and Affiliations

  1. Theory Center, Jefferson Lab, Newport News, VA, 23606, USA

    Alex Gnech

  2. Physics Division and Leadership Computing Facility, Argonne National Laboratory, Argonne, Lemont, IL, 60439, USA

    Corey Adams

  3. Physics Division, Argonne National Laboratory, Argonne, Lemont, IL, 60439, USA

    Nicholas Brawand

  4. Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland

    Giuseppe Carleo

  5. Physics Division and Computational Science Division, Argonne National Laboratory, Argonne, Lemont, IL, 60439, USA

    Alessandro Lovato

  6. Theoretical Physics Department, Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL, 60510, USA

    Noemi Rocco

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  1. Alex Gnech
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Correspondence to Alessandro Lovato.

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Gnech, A., Adams, C., Brawand, N. et al. Nuclei with Up to \(\varvec{A=6}\) Nucleons with Artificial Neural Network Wave Functions. Few-Body Syst 63, 7 (2022). https://doi.org/10.1007/s00601-021-01706-0

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