Litcius/Paper detail

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

Alessandro Pastore, Duncan Neill, H. D. Powell, K. Medler, C. J. Barton

2020Physical review. C29 citationsDOIOpen Access PDF

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 by roughly 40%. We then use the resulting models to predict the composition of the outer crust of a nonaccreting neutron star. We provide a first estimate of the impact of error propagation on the resulting equation of state of the system.

Topics & Concepts

Neutron starCrustMonte Carlo methodEquation of statePhysicsStatistical physicsNeutronMean squared errorStar (game theory)Propagation of uncertaintyArtificial neural networkBivariate analysisMultilayer perceptronComputational physicsAstrophysicsNuclear physicsMathematicsComputer scienceStatisticsThermodynamicsGeophysicsMachine learningPulsars and Gravitational Waves ResearchNuclear physics research studiesGamma-ray bursts and supernovae