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

TL;DR

This paper analyzes the uncertainties in nuclear mass models and their effect on predicting the outer crust composition of neutron stars, combining Monte Carlo error analysis with neural network corrections.

Contribution

It introduces a comprehensive error analysis using Monte Carlo methods and neural networks to improve predictions of neutron star crust composition.

Findings

Error bars on binding energies are more realistic after correlation analysis.

Neural network correction reduces model RMS error by approximately 40%.

Uncertainty propagation impacts the predicted equation of state for neutron star crusts.

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.