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Machine learning the deuteron

Keeble, J.W.T. and Rios, A. (2020) Machine learning the deuteron Physics Letters, Section B, 809, 135743.

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Abstract

We use machine learning techniques to solve the nuclear two-body bound state problem, the deuteron. We use a minimal one-layer, feed-forward neural network to represent the deuteron S- and D-state wavefunction in momentum space, and solve the problem variationally using ready-made machine learning tools. We benchmark our results with exact diagonalisation solutions. We find that a network with 6 hidden nodes (or 24 parameters) can provide a faithful representation of the ground state wavefunction, with a binding energy that is within 0.1% of exact results. This exploratory proof-of principle simulation may provide insight for future potential solutions of the nuclear many-body problem using variational artificial neural network techniques.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Physics
Authors :
NameEmailORCID
Keeble, J.W.T.j.keeble@surrey.ac.uk
Rios, A.A.Rios@surrey.ac.uk
Date : 10 September 2020
Funders : Science and Technology Facilities Council (STFC)
DOI : 10.1016/j.physletb.2020.135743
Copyright Disclaimer : © 2020 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Funded by SCOAP3.
Uncontrolled Keywords : Deuteron; Quantum many-body theory; Machine learning Neural networks
Depositing User : Diane Maxfield
Date Deposited : 01 Oct 2020 12:59
Last Modified : 11 Oct 2020 02:08
URI: http://epubs.surrey.ac.uk/id/eprint/858610

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