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.

References in corpus (14)

• GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
• Multi-messenger Observations of a Binary Neutron Star Merger
• Quark matter in compact stars?
• Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects
• Neutron conduction in the inner crust of a neutron star in the framework of the band theory of solids
• Further improvements on a global nuclear mass model
• Inner crust of neutron stars with mass-fitted Skyrme functionals
• Uncertainty Quantification for Nuclear Density Functional Theory and Information Content of New Measurements
• Impact of the symmetry energy on the outer crust of non-accreting neutron stars
• Validating neural-network refinements of nuclear mass models
• Error Analysis in Nuclear Density Functional Theory
• Bootstrapping the statistical uncertainties of NN scattering data
• A new statistical method for the structure of the inner crust of neutron stars
• Propagation of uncertainties in the nuclear DFT models

Cited by in corpus (10)

• 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