Extrapolation to infinite model space of no-core shell model calculations using machine learning
This work addresses the computational challenge of infinite model space extrapolation in nuclear physics, representing an incremental improvement with specific gains for light nuclei studies.
The researchers tackled the problem of extrapolating no-core shell model (NCSM) calculations to infinite model space for light nuclei using an ensemble of neural networks, achieving ground-state energies within a few hundred keV of experimental values and convergent radii for bound states.
An ensemble of neural networks is employed to extrapolate no-core shell model (NCSM) results to infinite model space for light nuclei. We present a review of our neural network extrapolations of the NCSM results obtained with the Daejeon16 NN interaction in different model spaces and with different values of the NCSM basis parameter $\hbarΩ$ for energies of nuclear states and root-mean-square (rms) radii of proton, neutron and matter distributions in light nuclei. The method yields convergent predictions with quantifiable uncertainties. Ground-state energies for $^{6}$Li, $^{6}$He, and the unbound $^{6}$Be, as well as the excited $(3^{+},0)$ and $(0^{+},1)$ states of $^{6}$Li, are obtained within a few hundred keV of experiment. The extrapolated radii of bound states converge well. In contrast, radii of unbound states in $^{6}$Be and $^{6}$Li do not stabilize.