Nuclei with up to $\boldsymbol{A=6}$ nucleons with artificial neural network wave functions

• URL: http://arxiv.org/abs/2108.06836v1
• Date: Sun, 15 Aug 2021 23:02:39 GMT
• Title: Nuclei with up to $\boldsymbol{A=6}$ nucleons with artificial neural network wave functions
• Authors: Alex Gnech, Corey Adams, Nicholas Brawand, Giuseppe Carleo, Alessandro Lovato and Noemi Rocco
• Abstract summary: We use artificial neural networks to compactly represent the wave functions of nuclei. We benchmark their binding energies, point-nucleon densities, and radii with the highly accurate hyperspherical harmonics method.
• Score: 52.77024349608834
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 Schr\"odinger 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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