Journal
PHYSICS LETTERS B
Volume 809, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.physletb.2020.135743
Keywords
Deuteron; Quantum many-body theory; Machine learning; Neural networks
Funding
- UK Science and Technology Facilities Council (STFC) [ST/P005314/1]
- STFC [ST/P005314/1] Funding Source: UKRI
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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. (C) 2020 The Author(s). Published by Elsevier B.V.
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