Machine learning the deuteron
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Keeble, J.W.T. and Rios, A. (2020) Machine learning the deuteron Physics Letters, Section B, 809, 135743.
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Official URL: http://dx.doi.org/10.1016/j.physletb.2020.135743
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 | |||||||||
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| Divisions : | Faculty of Engineering and Physical Sciences > Physics | |||||||||
| Authors : |
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| 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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