4.1 Article

Nuclei with Up to A=6 Nucleons with Artificial Neural Network Wave Functions

期刊

FEW-BODY SYSTEMS
卷 63, 期 1, 页码 -

出版社

SPRINGER WIEN
DOI: 10.1007/s00601-021-01706-0

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资金

  1. U.S. Department of Energy, Office of Science, Office of Nuclear Physics [DE-AC05-06OR23177, DE-AC02-06CH11357]
  2. NUCLEI SciDAC program
  3. U.S. Department of Energy, Office of Science, Office of High Energy Physics [DE-AC02-07CH11359]
  4. DOE Early Career Research Program
  5. Argonne LDRD awards
  6. INFN [INNN3]
  7. European Union [824093]
  8. DOE [DE-AC02-06CH11357]

向作者/读者索取更多资源

The groundbreaking works of Weinberg have paved the way for calculating atomic nuclei using systematically improvable Hamiltonians, with the help of artificial neural networks.
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 Schrodinger 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 Li-6 and He-6 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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