From NS observations to nuclear matter properties: a machine learning approach

Abstract

This study is devoted to the inference problem of extracting the nuclear matter properties directly from a set of mass-radius observations. We employ Bayesian neural networks (BNNs), which is a probabilistic model capable of estimating the uncertainties associated with its predictions. To simulate different noise levels on the $M(R)$ observations, we create three different sets of mock data. Our results show BNNs as an accurate and reliable tool for predicting the nuclear matter properties whenever the true values are not completely outside the training dataset statistics, i.e., if the model is not heavily dependent on its extrapolating capacities. Using real mass-radius pulsar data, the model predicted, for instance, $L_{\text{sym}}=39.80\pm 17.52$ MeV and $K_{\text{sym}}=-101.67\pm 62.86$ MeV ($2\sigma$ interval). Our study provides a valuable inference framework when new NS data becomes available.

15 pages, 12 figures

References in corpus (17)

• Shapiro delay measurement of a two solar mass neutron star
• A Massive Pulsar in a Compact Relativistic Binary
• GW190814: Gravitational Waves from the Coalescence of a 23 M${}_{\odot }$ Black Hole with a 2.6 M${}_{\odot }$ Compact Object
• PSR J0030+0451 Mass and Radius from NICER Data and Implications for the Properties of Neutron Star Matter
• A NICER View of PSR J0030+0451: Millisecond Pulsar Parameter Estimation
• Neutron-Rich Nuclei in Heaven and Earth
• Relativistic Mean-Field Hadronic Models under Nuclear Matter Constraints
• Progress in Constraining Nuclear Symmetry Energy Using Neutron Star Observables Since GW170817
• Density dependence of the nuclear symmetry energy: a microscopic perspective
• PSR J1810+1744: Companion Darkening and a Precise High Neutron Star Mass
• Relativistic description of dense matter equation of state and compatibility with neutron star observables: a Bayesian approach
• Interdependence of different symmetry energy elements
• Neural networks reconstruction of the dense-matter equation of state from neutron-star parameters
• Translating neutron star observations to nuclear symmetry energy via artificial neural networks
• Empirical constraints on the high-density equation of state from multi-messenger observables
• Extracting nuclear matter properties from the neutron star matter equation of state using deep neural networks
• Determination of the symmetry energy from the neutron star equation of state

Cited by in corpus (9)

• Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics
• Dense Matter in Neutron Stars with eXTP
• Bayesian Inference of Fine-Features of Nuclear Equation of State from Future Neutron Star Radius Measurements to 0.1km Accuracy
• Implications of latest NICER data for the neutron star equation of state
• Constraining neutron star matter from the slope of the mass-radius curves
• Neural Simulation-Based Inference of the Neutron Star Equation of State directly from Telescope Spectra
• Deep learning inference of the neutron star equation of state
• Detecting Hyperons in neutron stars -- a machine learning approach
• Conditional variational autoencoder inference of neutron star equation of state from astrophysical observations