Machine learning approach to pattern recognition in nuclear dynamics from the ab initio symmetry-adapted no-core shell model

0556226 - ÚJF 2023 RIV US eng J - Článek v odborném periodiku
Molchanov, O. M. - Launey, K. D. - Marcenne, A. - Sargsyan, G. H. - Dytrych, Tomáš - Draayer, J. P.
Machine learning approach to pattern recognition in nuclear dynamics from the ab initio symmetry-adapted no-core shell model.
Physical Review C. Roč. 105, č. 3 (2022), č. článku 034306. ISSN 2469-9985. E-ISSN 2469-9993
Grant CEP: GA ČR(CZ) GA22-14497S
Institucionální podpora: RVO:61389005
Klíčová slova: dynamics * symmetry * no-core shell model
Obor OECD: Nuclear physics
Impakt faktor: 3.1, rok: 2022
Způsob publikování: Omezený přístup
https://doi.org/10.1103/PhysRevC.105.034306

A novel machine learning approach is used to provide further insight into atomic nuclei and to detect orderly patterns amid a vast data of large-scale calculations. The method utilizes a neural network that is trained on ab initio results from the symmetry-adapted no-core shell model (SA-NCSM) for light nuclei. We show that the SA-NCSM, which expands ab initio applications up to medium-mass nuclei by using dominant symmetries of nuclear dynamics, can reach heavier nuclei when coupled with the machine learning approach. In particular, we find that a neural network trained on probability amplitudes for s- and p-shell nuclear wave functions not only predicts dominant configurations for heavier nuclei but in addition, when tested for the Ne-20 ground state, accurately reproduces the probability distribution. The non-negligible configurations predicted by the network provide an important input to the SA-NCSM for reducing ultralarge model spaces to manageable sizes that can be, in turn, utilized in SA-NCSM calculations to obtain accurate observables. The neural network is capable of describing nuclear deformation and is used to track the shape evolution along the Mg20-42 isotopic chain, suggesting a shape coexistence that is more pronounced toward the very neutron-rich isotopes. We provide first descriptions of the structure and deformation of Si-24 and Mg-40 of interest to x-ray burst nucleosynthesis, and even of the extremely heavy nuclei such as Er-166,Er-168 and U-236, that build on first-principles considerations.
Trvalý link: http://hdl.handle.net/11104/0330515