Decoding Neutron Star Observations: Revealing Composition through Bayesian Neural Networks

Abstract

We exploit the great potential offered by Bayesian Neural Networks (BNNs) to directly decipher the internal composition of neutron stars (NSs) based on their macroscopic properties. By analyzing a set of simulated observations, namely NS radius and tidal deformability, we leverage BNNs as effective tools for inferring the proton fraction and sound speed within NS interiors. To achieve this, several BNNs models were developed upon a dataset of $\sim$ 25K nuclear EoS within a relativistic mean-field framework, obtained through Bayesian inference that adheres to minimal low-density constraints. Unlike conventional neural networks, BNNs possess an exceptional quality: they provide a prediction uncertainty measure. To simulate the inherent imperfections present in real-world observations, we have generated four distinct training and testing datasets that replicate specific observational uncertainties. Our initial results demonstrate that BNNs successfully recover the composition with reasonable levels of uncertainty. Furthermore, using mock data prepared with the DD2, a different class of relativistic mean-field model utilized during training, the BNN model effectively retrieves the proton fraction and speed of sound for neutron star matter.

16 pages, 15 figures, published version

References in corpus (16)

• 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
• PSR J1810+1744: Companion Darkening and a Precise High Neutron Star Mass
• Strong correlations of neutron star radii with the slopes of nuclear matter incompressibility and symmetry energy at saturation
• Relativistic description of dense matter equation of state and compatibility with neutron star observables: a Bayesian approach
• Neural networks reconstruction of the dense-matter equation of state from neutron-star parameters
• Thermal evolution of relativistic hyperonic compact stars with calibrated equations of state
• 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

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