Machine learning the deuteron: new architectures and uncertainty quantification

Abstract

We solve the ground state of the deuteron using a variational neural network ansatz for the wavefunction in momentum space. This ansatz provides a flexible representation of both the S and the D states, with relative errors in the energy which are within fractions of a percent of a full diagonalization benchmark. We extend the previous work on this area in two directions. First, we study new architectures by adding more layers to the network and by exploring different connections between the states. Second, we provide a better estimate of the numerical uncertainty by taking into account the final oscillations at the end of the minimization process. Overall, we find that the best performing architecture is the simple one-layer, state-disconnected network. Two-layer networks show indications of overfitting, in regions that are not probed by the fixed momentum basis where calculations are performed. In all cases, the errors associated to the model oscillations around the real minimum are larger than the stochastic initilization uncertainties.