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Title: Extrapolation of nuclear structure observables with artificial neural networks

Abstract

Calculations of nuclei are often carried out in finite model spaces. Thus, finite-size corrections enter, and it is necessary to extrapolate the computed observables to infinite model spaces. In this work, we employ extrapolation methods based on artificial neural networks for observables such as the ground-state energy and the point-proton radius. We extrapolate results from no-core shell model and coupled-cluster calculations to very large model spaces and estimate uncertainties. Training the network on different data typically yields extrapolation results that cluster around distinct values. We show that a preprocessing of input data, and the inclusion of correlations among the input data, reduces the problem of multiple solutions and yields more stable extrapolated results and consistent uncertainty estimates. We perform extrapolations for ground-state energies and radii in 4He, 6Li, and 16O, and compare the predictions from neural networks with results from infrared extrapolations.

Authors:
ORCiD logo; ; ORCiD logo
Publication Date:
Research Org.:
Univ. of Tennessee, Knoxville, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Nuclear Physics (NP) (SC-26)
OSTI Identifier:
1593553
Grant/Contract Number:  
FG02-96ER40963
Resource Type:
Accepted Manuscript
Journal Name:
Physical Review C
Additional Journal Information:
Journal Volume: 100; Journal Issue: 5; Journal ID: ISSN 2469-9985
Publisher:
American Physical Society (APS)
Country of Publication:
United States
Language:
English
Subject:
73 NUCLEAR PHYSICS AND RADIATION PHYSICS

Citation Formats

Jiang, W. G., Hagen, G., and Papenbrock, T. Extrapolation of nuclear structure observables with artificial neural networks. United States: N. p., 2019. Web. doi:10.1103/PhysRevC.100.054326.
Jiang, W. G., Hagen, G., & Papenbrock, T. Extrapolation of nuclear structure observables with artificial neural networks. United States. doi:10.1103/PhysRevC.100.054326.
Jiang, W. G., Hagen, G., and Papenbrock, T. Thu . "Extrapolation of nuclear structure observables with artificial neural networks". United States. doi:10.1103/PhysRevC.100.054326.
@article{osti_1593553,
title = {Extrapolation of nuclear structure observables with artificial neural networks},
author = {Jiang, W. G. and Hagen, G. and Papenbrock, T.},
abstractNote = {Calculations of nuclei are often carried out in finite model spaces. Thus, finite-size corrections enter, and it is necessary to extrapolate the computed observables to infinite model spaces. In this work, we employ extrapolation methods based on artificial neural networks for observables such as the ground-state energy and the point-proton radius. We extrapolate results from no-core shell model and coupled-cluster calculations to very large model spaces and estimate uncertainties. Training the network on different data typically yields extrapolation results that cluster around distinct values. We show that a preprocessing of input data, and the inclusion of correlations among the input data, reduces the problem of multiple solutions and yields more stable extrapolated results and consistent uncertainty estimates. We perform extrapolations for ground-state energies and radii in 4He, 6Li, and 16O, and compare the predictions from neural networks with results from infrared extrapolations.},
doi = {10.1103/PhysRevC.100.054326},
journal = {Physical Review C},
number = 5,
volume = 100,
place = {United States},
year = {2019},
month = {11}
}

Journal Article:
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