Machine learning light hypernuclei
arXiv:2203.11792 · doi:10.1016/j.nuclphysa.2023.122625
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 separation energy of the lightest hypernuclei, H, H and 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 separation energy to model spaces of size . The results obtained are in agreement with the experimental data in the case of H and the state of He, although they are off of the experiment by about MeV for both the and states of H and the state of 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.
arXiv comment16 pages, 7 figures, 1 table. Accepted for publication in Nuclear Physics A
References in corpus (36)
- Scikit-learn: Machine Learning in Python
- Machine learning and the physical sciences
- Accurate Charge-Dependent Nucleon-Nucleon Potential at Fourth Order of Chiral Perturbation Theory
- A high-bias, low-variance introduction to Machine Learning for physicists
- Strangeness in nuclear physics
- 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 Mass Predictions for the Crustal Composition of Neutron Stars: A Bayesian Neural Network Approach
- Bayesian approach to model-based extrapolation of nuclear observables
- Neutron drip line in the Ca region from Bayesian model averaging
- Predictions of nuclear -decay half-lives with machine learning and their impacts on process
- Nuclear charge radii: Density functional theory meets Bayesian neural networks
- Machine learning the nuclear mass
- Nuclear mass systematics using neural networks
- Taming nuclear complexity with a committee of multilayer neural networks
- Ab Initio Description of p-Shell Hypernuclei
- 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
- Methodology study of machine learning for the neutron star equation of state
- Deep learning: Extrapolation tool for ab initio nuclear theory
- Machine learning the deuteron
- Nuclear masses in extended kernel ridge regression with odd-even effects
- Extrapolation of nuclear structure observables with artificial neural networks
- Bayesian evaluation of charge yields of fission fragments of 239U
- Large-scale exact diagonalizations reveal low-momentum scales of nuclei
- Unveiling the nuclear matter EoS from neutron star properties: a supervised machine learning approach
- Estimation of fusion-evaporation cross sections by artificial neural networks
- Hypernuclear No-Core Shell Model
- Nuclear binding energy predictions using neural networks: Application of the multilayer perceptron
- The Proton Radius from Bayesian Inference
- Nucleon axial form factor from a Bayesian neural-network analysis of neutrino-scattering data
- Trees and Forests in Nuclear Physics
- Nuclear physics uncertainties in light hypernuclei
- Systematic Nuclear Uncertainties in the Hypertriton System