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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61389005%3A_____%2F22%3A00556226" target="_blank" >RIV/61389005:_____/22:00556226 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1103/PhysRevC.105.034306" target="_blank" >https://doi.org/10.1103/PhysRevC.105.034306</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1103/PhysRevC.105.034306" target="_blank" >10.1103/PhysRevC.105.034306</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10304 - Nuclear physics

Project

<a href="/en/project/GA22-14497S" target="_blank" >GA22-14497S: Advancing the frontiers of first-principle modeling of atomic nuclei</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Physical Review C

ISSN

2469-9985

e-ISSN

2469-9993

Volume of the periodical

105

Issue of the periodical within the volume

3

Country of publishing house

US - UNITED STATES

Number of pages

10

Pages from-to

034306

UT code for WoS article

000767144000001

EID of the result in the Scopus database

2-s2.0-85126700632