Impact of statistical uncertainties on the composition of the outer crust of a neutron star

Abstract

By means of Monte Carlo methods, we perform a full error analysis on the Duflo-Zucker mass model. In particular, we study the presence of correlations in the residuals to obtain a more realistic estimate of the error bars on the predicted binding energies. To further reduce the discrepancies between model prediction and experimental data we also apply a Multilayer Perceptron Neural Network. We show that the root mean square of the model further reduces of roughly 40\%. We then use the resulting models to predict the composition of the outer crust of a non accreting neutron star. We provide a first estimate of the impact of error propagation on the resulting equation of state of the system.

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• Machine Learning in Nuclear Physics
• Machine learning the nuclear mass
• Trees and Forests in Nuclear Physics
• Analytical determination of the structure and nuclear abundances of the outer crust of a cold nonaccreted neutron star
• Extrapolating from neural network models: a cautionary tale
• Nuclear mass predictions based on convolutional neural network
• Systematic analysis of inner crust composition using the extended Thomas-Fermi approximation with pairing correlations
• Probing phase transition in neutron stars via the crust-core interfacial mode
• Comparison between the Thomas-Fermi and Hartree-Fock-Bogoliubov Methods in the Inner Crust of a Neutron Star: The Role of Pairing Correlations
• Report from the A.I. For Nuclear Physics Workshop