Backflow Transformation for A=3 Nuclei with Artificial Neural Networks
Backflow Transformation for A=3 Nuclei with Artificial Neural Networks
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摘要: A novel variational wave function defined as a Jastrow factor multiplying a backflow transformed Slater determinant was developed for A=3 nuclei. The Jastrow factor and backflow transformation were represented by artificial neural networks. With this newly developed wave function, variational Monte Carlo calculations were carried out for 3H and 3He nuclei starting from a nuclear Hamiltonian based on the leading-order pionless effective field theory. The obtained ground-state energy and charge radii were successfully benchmarked against the results of the highly-accurate hyperspherical-harmonics method. The backflow transformation plays a crucial role in improving the nodal surface of the Slater determinant and, thus, providing accurate groundstate energy.Abstract: A novel variational wave function defined as a Jastrow factor multiplying a backflow transformed Slater determinant was developed for A=3 nuclei. The Jastrow factor and backflow transformation were represented by artificial neural networks. With this newly developed wave function, variational Monte Carlo calculations were carried out for 3H and 3He nuclei starting from a nuclear Hamiltonian based on the leading-order pionless effective field theory. The obtained ground-state energy and charge radii were successfully benchmarked against the results of the highly-accurate hyperspherical-harmonics method. The backflow transformation plays a crucial role in improving the nodal surface of the Slater determinant and, thus, providing accurate ground-state energy.