Extracting nuclear matter properties from the neutron star matter equation of state using deep neural networks

Abstract

The extraction of the nuclear matter properties from neutron star (NS) observations is nowadays an important issue, in particular, the properties that characterize the symmetry energy which are essential to describe correctly asymmetric nuclear matter. We use deep neural networks (DNNs) to map the relation between cold $\beta$-equilibrium NS matter and the nuclear matter properties. Assuming a quadratic dependence on the isospin asymmetry for the energy per particle of homogeneous nuclear matter and using a Taylor expansion up to fourth order in the iso-scalar and iso-vector contributions, we generate a dataset of different realizations of $\beta$-equilibrium NS matter and the corresponding nuclear matter properties. The DNN model was successfully trained, attaining great accuracy in the test set. Finally, a real case scenario was used to test the DNN model, where a set of 33 nuclear models, obtained within a relativistic mean field approach or a Skyrme force description, were fed into the DNN model and the corresponding nuclear matter parameters recovered with considerable accuracy, in particular, the standard deviations $\sigma (L_{\text{sym}})=12.85$ MeV and $\sigma (K_{\text{sat}})=41.02$ MeV were obtained, respectively, for the slope of the symmetry energy and the nuclear matter incompressibility at saturation.

10 pages, 5 figures; published version

References in corpus (28)

• 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
• A NICER View of the Massive Pulsar PSR J0740+6620 Informed by Radio Timing and XMM-Newton Spectroscopy
• Refined Mass and Geometric Measurements of the High-Mass PSR J0740+6620
• Nonparametric constraints on neutron star matter with existing and upcoming gravitational wave and pulsar observations
• Neutron Stars and the Nuclear Equation of State
• Progress in Constraining Nuclear Symmetry Energy Using Neutron Star Observables Since GW170817
• Spectral Representations of Neutron-Star Equations of State
• Density dependence of the nuclear symmetry energy: a microscopic perspective
• Nonparametric Inference of Neutron Star Composition, Equation of State, and Maximum Mass with GW170817
• Astrophysical Constraints on the Symmetry Energy and the Neutron Skin of ${}^{208}$Pb with Minimal Modeling Assumptions
• PSR J1810+1744: Companion Darkening and a Precise High Neutron Star Mass
• A Detailed Examination of Astrophysical Constraints on the Symmetry Energy and the Neutron Skin of ${}^{208}$Pb with Minimal Modeling Assumptions
• Relativistic description of dense matter equation of state and compatibility with neutron star observables: a Bayesian approach
• Bayesian Inference of Dense Matter Equation of State within Relativistic Mean Field Models using Astrophysical Measurements
• Neural networks reconstruction of the dense-matter equation of state from neutron-star parameters
• Extensive Studies of the Neutron Star Equation of State from the Deep Learning Inference with the Observational Data Augmentation
• Neural network reconstruction of the dense matter equation of state from neutron star observables
• $\Delta$-admixed neutron stars: spinodal instabilities and dUrca processes
• Bayesian inference of signatures of hyperons inside neutron stars
• Translating neutron star observations to nuclear symmetry energy via artificial neural networks
• Bayesian reconstruction of nuclear matter parameters from the equation of state of neutron star matter
• Quark matter in light neutron stars
• A Bayesian analysis of the properties of hybrid stars with the NJL model
• Determination of the symmetry energy from the neutron star equation of state

Cited by in corpus (13)

• Exploring QCD matter in extreme conditions with Machine Learning
• Decoding Neutron Star Observations: Revealing Composition through Bayesian Neural Networks
• Nonparametric model for the equations of state of neutron star from deep neural network
• From NS observations to nuclear matter properties: a machine learning approach
• New covariant density functionals of nuclear matter for compact star simulations
• Analyzing the speed of sound in neutron star with machine learning
• 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
• Inferring the Equation of State from Neutron Star Observables via Machine Learning
• Detecting Hyperons in neutron stars -- a machine learning approach
• Explainable autoencoder for neutron star dense matter parameter estimation
• Approximating neutron-star radii using gravitational-wave only measurements with symbolic regression
• Conditional variational autoencoder inference of neutron star equation of state from astrophysical observations