Machine learning light hypernuclei

Abstract

We employ a feed-forward artificial neural network to extrapolate at large model spaces the results of {\it ab-initio} hypernuclear No-Core Shell Model calculations for the $\Lambda$ separation energy $B_{\Lambda }$ of the lightest hypernuclei, ${}_{\Lambda }^3$H, ${}_{\Lambda }^4$H and ${}_{\Lambda }^4$He, obtained in computationally accessible harmonic oscillator basis spaces using chiral nucleon-nucleon, nucleon-nucleon-nucleon and hyperon-nucleon interactions. The overfitting problem is avoided by enlarging the size of the input dataset and by introducing a Gaussian noise during the training process of the neural network. We find that a network with a single hidden layer of eight neurons is sufficient to extrapolate correctly the value of the $\Lambda$ separation energy to model spaces of size $N_{max}=100$. The results obtained are in agreement with the experimental data in the case of ${}_{\Lambda }^3$H and the $0^{+}$ state of ${}_{\Lambda }^4$He, although they are off of the experiment by about $0.3$ MeV for both the $0^{+}$ and $1^{+}$states of ${}_{\Lambda }^4$H and the $1^{+}$ state of ${}_{\Lambda }^4$He. We find that our results are in excellent agreement with those obtained using other extrapolation schemes of the No-Core Shell Model calculations, showing this that an ANN is a reliable method to extrapolate the results of hypernuclear No-Core Shell Model calculations to large model spaces.

16 pages, 7 figures, 1 table. Accepted for publication in Nuclear Physics A

References in corpus (14)

• Local three-nucleon interaction from chiral effective field theory
• Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects
• Hyperon-nucleon interactions - a chiral effective field theory approach
• Nuclear charge radii: Density functional theory meets Bayesian neural networks
• Machine learning the nuclear mass
• Applications and Techniques for Fast Machine Learning in Science
• An artificial neural network application on nuclear charge radii
• Decoding Beta-Decay Systematics: A Global Statistical Model for Beta^- Halflives
• Nuclear masses in extended kernel ridge regression with odd-even effects
• Bayesian evaluation of charge yields of fission fragments of 239U
• Nuclear binding energy predictions using neural networks: Application of the multilayer perceptron
• The Proton Radius from Bayesian Inference
• Nuclear physics uncertainties in light hypernuclei
• Systematic Nuclear Uncertainties in the Hypertriton System

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