Neural networks reconstruction of the dense-matter equation of state from neutron-star parameters

Abstract

Aims: The aim of this work is to study the application of the artificial neural networks guided by the autoencoder architecture as a method for precise reconstruction of the neutron star equation of state, using their observable parameters: masses, radii and tidal deformabilities. In addition we study how well the neutron star radius can be reconstructed using the gravitational-wave only observations of tidal deformability, i.e. quantities which are not related in a straightforward way. Methods: Application of artificial neural network in the equation of state reconstruction exploits the non-linear potential of this machine learning model. Since each neuron in the network is basically a non-linear function, it is possible to create a complex mapping between the input sets of observations and the output equation of state table. Within the supervised training paradigm, we construct a few hidden layer deep neural network on a generated data set, consisting of a realistic equation of state for the neutron star crust connected with a piecewise relativistic polytropes dense core, with parameters representative to the state-of-the art realistic equations of state. Results: We demonstrate the performance of our machine learning implementation with respect to the simulated cases with varying number of observations and measurement uncertainties. Furthermore we study the impact of the neutron star mass distributions on the results. Finally, we test the reconstruction of the equation of state trained on parametric polytropic training set using the simulated mass--radius and mass--tidal-deformability sequences based on realistic equations of state. Neural networks trained with a limited data set are able to generalize the mapping between global parameters and equation of state input tables for realistic models.

arXiv comment8, pages, 7 figures, accepted in Astronomy and Astrophysics

References in corpus (31)

• GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
• Shapiro delay measurement of a two solar mass neutron star
• A Massive Pulsar in a Compact Relativistic Binary
• Gravitational Waves and Gamma-rays from a Binary Neutron Star Merger: GW170817 and GRB 170817A
• The equation of state for nucleon matter and neutron star structure
• GW170817: Measurements of Neutron Star Radii and Equation of State
• 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
• GW190425: Observation of a Compact Binary Coalescence with Total Mass $\sim 3.4M_{\odot }$
• Relativistic Shapiro delay measurements of an extremely massive millisecond pulsar
• A unified equation of state of dense matter and neutron star structure
• Constraining neutron star tidal Love numbers with gravitational wave detectors
• Science Case for the Einstein Telescope
• The Equation of State from Observed Masses and Radii of Neutron Stars
• The NANOGrav Nine-year Data Set: Mass and Geometric Measurements of Binary Millisecond Pulsars
• Tidal Deformabilities and Radii of Neutron Stars from the Observation of GW170817
• Further explorations of Skyrme-Hartree-Fock-Bogoliubov mass formulas. XII: Stiffness and stability of neutron-star matter
• The Neutron Star Mass-Radius Relation and the Equation of State of Dense Matter
• Systematic and statistical errors in a bayesian approach to the estimation of the neutron-star equation of state using advanced gravitational wave detectors
• Evidence for a maximum mass cut-off in the neutron star mass distribution and constraints on the equation of state
• Mapping neutron star data to the equation of state using the deep neural network
• From Neutron Star Observables to the Equation of State. I. An Optimal Parametrization
• Bayesian Inference of Dense Matter Equation of State within Relativistic Mean Field Models using Astrophysical Measurements
• Constraining the neutron star equation of state using multi-band independent measurements of radii and tidal deformabilities
• Methodology study of machine learning for the neutron star equation of state
• Measuring the neutron star equation of state with gravitational waves: the first forty binary neutron star mergers
• Tidal deformability and other global parameters of compact stars with strong phase transitions
• Upper limit set by causality on the tidal deformability of a neutron star
• Predicting the properties of black holes merger remnants with Deep Neural Networks
• The key factor to determine the relation between radius and tidal deformability of neutron stars: slope of symmetry energy
• Dense matter equation of state and neutron star properties from nuclear theory and experiment

Cited by in corpus (34)

• Neutron star tidal deformability and equation of state constraints
• Exploring QCD matter in extreme conditions with Machine Learning
• Bayesian analysis of neutron-star properties with parameterized equations of state: the role of the likelihood functions
• Inference of the sound speed and related properties of neutron stars
• 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
• Reconstructing the neutron star equation of state from observational data via automatic differentiation
• Unveiling the nuclear matter EoS from neutron star properties: a supervised machine learning approach
• Bayesian inference of signatures of hyperons inside neutron stars
• Translating neutron star observations to nuclear symmetry energy via artificial neural networks
• Tidal deformability of strange stars and the GW170817 event
• Probing elastic quark phases in hybrid stars with radius measurements
• Dense Matter in Neutron Stars with eXTP
• Deducing Neutron Star Equation of State Parameters Directly From Telescope Spectra with Uncertainty-Aware Machine Learning
• Extracting nuclear matter properties from the neutron star matter equation of state using deep neural networks
• Structure of Quark Star: A Comparative Analysis of Bayesian Inference and Neural Network Based Modeling
• Decoding Neutron Star Observations: Revealing Composition through Bayesian Neural Networks
• From NS observations to nuclear matter properties: a machine learning approach
• Analyzing the speed of sound in neutron star with machine learning
• High-Precision Inversion of Dynamic Radiography Using Hydrodynamic Features
• Neural Simulation-Based Inference of the Neutron Star Equation of State directly from Telescope Spectra
• Analyzing the Galactic pulsar distribution with machine learning
• Detecting dense-matter phase transition signatures in neutron star mass-radius measurements as data anomalies using normalising flows
• Realizing the potential of deep neural network for analyzing neutron star observables and dense matter equation of state
• Deep learning inference of the neutron star equation of state
• Inferring the Equation of State from Neutron Star Observables via Machine Learning
• Mass and tidal parameter extraction from gravitational waves of binary neutron stars mergers using deep 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
• Properties of the Object HESS J1731-347 as a Twin Compact Star
• Nuclear mass table in density functional approach inspired by neutron-star observations
• Neural Network Representations of Multiphase Equations of State