Pacific National University

About: Pacific National University is a education organization based out in Khabarovsk, Russia. It is known for research contribution in the topics: Control system & Scattering. The organization has 474 authors who have published 515 publications receiving 1490 citations.

Prediction for a Four-Neutron Resonance.

Abstract: We utilize various ab initio approaches to search for a low-lying resonance in the four-neutron (4n) system using the JISP16 realistic NN interaction. Our most accurate prediction is obtained using a J-matrix extension of the no-core shell model and suggests a 4n resonant state at an energy near E_{r}=0.8 MeV with a width of approximately Γ=1.4 MeV.

Ab initio effective interactions for s d -shell valence nucleons

Abstract: We perform ab initio no-core shell-model calculations for $A=18$ and 19 nuclei in a $4\ensuremath{\hbar}\mathrm{\ensuremath{\Omega}}$, or ${N}_{\mathrm{max}}=4$, model space by using the effective JISP16 and chiral N3LO nucleon-nucleon potentials and transform the many-body effective Hamiltonians into the $0\ensuremath{\hbar}\mathrm{\ensuremath{\Omega}}$ model space to construct the $A$-body effective Hamiltonians in the $sd$ shell. We separate the $A$-body effective Hamiltonians with $A=18$ and $A=19$ into inert core, one-, and two-body components. Then we use these core, one-, and two-body components to perform standard shell-model calculations for the $A=18$ and $A=19$ systems with valence nucleons restricted to the $sd$ shell. Finally, we compare the standard shell-model results in the $0\ensuremath{\hbar}\mathrm{\ensuremath{\Omega}}$ model space with the exact no-core shell-model results in the $4\ensuremath{\hbar}\mathrm{\ensuremath{\Omega}}$ model space for the $A=18$ and $A=19$ systems and find good agreement.

Deep learning: Extrapolation tool for ab initio nuclear theory

Abstract: Ab initio approaches in nuclear theory, such as the no-core shell model (NCSM), have been developed for approximately solving finite nuclei with realistic strong interactions. The NCSM and other approaches require an extrapolation of the results obtained in a finite basis space to the infinite basis space limit and assessment of the uncertainty of those extrapolations. Each observable requires a separate extrapolation and many observables have no proven extrapolation method. We propose a feed-forward artificial neural network (ANN) method as an extrapolation tool to obtain the ground-state energy and the ground-state point-proton root-mean-square (rms) radius along with their extrapolation uncertainties. The designed ANNs are sufficient to produce results for these two very different observables in $^{6}\mathrm{Li}$ from the ab initio NCSM results in small basis spaces that satisfy the following theoretical physics condition: independence of basis space parameters in the limit of extremely large matrices. Comparisons of the ANN results with other extrapolation methods are also provided.