Methodology study of machine learning for the neutron star equation of state

Abstract

We discuss a methodology of machine learning to deduce the neutron star equation of state from a set of mass-radius observational data. We propose an efficient procedure to deal with a mapping from finite data points with observational errors onto an equation of state. We generate training data and optimize the neural network. Using independent validation data (mock observational data) we confirm that the equation of state is correctly reconstructed with precision surpassing observational errors. We finally discuss the relation between our method and Bayesian analysis with an emphasis put on generality of our method for underdetermined problems.

5 pages, 4 figures

References in corpus (13)

• Shapiro delay measurement of a two solar mass neutron star
• A Massive Pulsar in a Compact Relativistic Binary
• Constraints on a phenomenologically parameterized neutron-star equation of state
• Astrophysical Measurement of the Equation of State of Neutron Star Matter
• Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data
• Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects
• Spectral Representations of Neutron-Star Equations of State
• New Critical Point Induced by the Axial Anomaly in Dense QCD
• Compact stars with sequential QCD phase transitions
• Glitch Classification and Clustering for LIGO with Deep Transfer Learning
• What is the Machine Learning?
• Massive Hybrid Quark Stars with Strong Magnetic Field
• Parameter estimates in binary black hole collisions using neural networks

Cited by in corpus (50)

• Trace anomaly as signature of conformality in neutron stars
• Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
• Gravitational Wave Denoising of Binary Black Hole Mergers with Deep Learning
• Mapping neutron star data to the equation of state using the deep neural network
• Exploring QCD matter in extreme conditions with Machine Learning
• Bayesian Inference of Dense Matter Equation of State within Relativistic Mean Field Models using Astrophysical Measurements
• Precision Studies of QCD in the Low Energy Domain of the EIC
• Hybrid and quark star matter based on a non-perturbative equation of state
• Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning
• 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
• Phase Transition Phenomenology with Nonparametric Representations of the Neutron Star Equation of State
• 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
• Bayesian nonparametric inference of neutron star equation of state via neural network
• Unveiling the nuclear matter EoS from neutron star properties: a supervised machine learning approach
• Momentum Shell in Quarkyonic Matter from Explicit Duality: A Dual Model for Cold, Dense QCD
• Fusing numerical relativity and deep learning to detect higher-order multipole waveforms from eccentric binary black hole mergers
• Bayesian inference of signatures of hyperons inside neutron stars
• Equation of state of cold and dense QCD matter in resummed perturbation theory
• 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
• Continuity from neutron matter to two-flavor quark matter with ${}^1{S}_0$ and ${}^3{P}_2$ superfluidity
• Determination of the symmetry energy from the neutron star equation of state
• Decoding Neutron Star Observations: Revealing Composition through Bayesian Neural Networks
• Applications of machine learning in gravitational wave research with current interferometric detectors
• Unified neutron star equations of state calibrated to nuclear properties
• Nonparametric model for the equations of state of neutron star from deep neural network
• Continuous-mixture Autoregressive Networks for efficient variational calculation of many-body systems
• From NS observations to nuclear matter properties: a machine learning approach
• Analyzing the speed of sound in neutron star with machine learning
• Training multi-objective/multi-task collocation physics-informed neural network with student/teachers transfer learnings
• Neural Simulation-Based Inference of the Neutron Star Equation of State directly from Telescope Spectra
• Nonparametric Representation of Neutron Star Equation of State Using Variational Autoencoder
• Machine learning light hypernuclei
• Deep learning inference of the neutron star equation of state
• Realizing the potential of deep neural network for analyzing neutron star observables and dense matter equation of state
• Explainable autoencoder for neutron star dense matter parameter estimation
• Continuity from neutron matter to color-superconducting quark matter with ${}^3{P}_2$ superfluidity
• Neutron star equation of state: identifying hadronic matter characteristics
• Detecting Hyperons in neutron stars -- a machine learning approach
• Direct nonparametric multimessenger constraints on the equation of state of cold dense nuclear matter
• Cluster Structures with Machine Learning Support in Neutron Star M-R relations
• From masses and radii of neutron stars to EOS of nuclear matter through neural network
• A Neural Network Perturbation Theory Based on the Born Series
• How machine learning conquers the unitary limit
• 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
• Anisotropic pressure effect on central EOS of PSR J0740+6620 in the light of dimensionless TOV equation
• Inferring the nuclear symmetry energy at supra saturation density from neutrino cooling