Deducing Neutron Star Equation of State Parameters Directly From Telescope Spectra with Uncertainty-Aware Machine Learning
arXiv:2209.02817 · doi:10.1088/1475-7516/2023/02/016
Abstract
Neutron stars provide a unique laboratory for studying matter at extreme pressures and densities. While there is no direct way to explore their interior structure, X-rays emitted from these stars can indirectly provide clues to the equation of state (EOS) of superdense nuclear matter through the inference of the star's mass and radius. However, inference of EOS directly from a star's X-ray spectra is extremely challenging and is complicated by systematic uncertainties. The current state of the art is to use simulation-based likelihoods in a piece-wise method, which first infer the star's mass and radius to reduce the dimensionality of the problem, and from those quantities infer the EOS. We demonstrate a series of enhancements to the state of the art, in terms of realistic uncertainty quantification and improved regression of physical properties with machine learning. We also demonstrate novel inference of the EOS directly from the high-dimensional spectra of observed stars, avoiding the intermediate mass-radius step. Our network is conditioned on the sources of uncertainty of each star, allowing for natural and complete propagation of uncertainties to the EOS.
arXiv comment24 pages, 20 figures
References in corpus (52)
- Adam: A Method for Stochastic Optimization
- Scikit-learn: Machine Learning in Python
- Neutron Star Structure and the Equation of State
- Chiral effective field theory and nuclear forces
- Color superconductivity in dense quark matter
- Searching for Exotic Particles in High-Energy Physics with Deep Learning
- Equations of state for supernovae and compact stars
- The frontier of simulation-based inference
- Composition and thermodynamics of nuclear matter with light clusters
- The Equation of State from Observed Masses and Radii of Neutron Stars
- Equation of state and neutron star properties constrained by nuclear physics and observation
- Neutron skin of 208Pb, nuclear symmetry energy, and the parity radius experiment
- Measuring the neutron star equation of state using X-ray timing
- Crustal Heating and Quiescent Emission from Transiently Accreting Neutron Stars
- A Hydrogen Atmosphere Spectral Model Applied to the Neutron Star X7 in the Globular Cluster 47 Tucanae
- The Compression-Mode Giant Resonances and Nuclear Incompressibility
- The Neutron Stars of Soft X-Ray Transients
- Neutron Star Masses and Radii from Quiescent Low-Mass X-ray Binaries
- Constraining the Mass and Radius of Neutron Stars in Globular Clusters
- Parameterized Machine Learning for High-Energy Physics
- Strangeness in Nuclei and Neutron Stars
- Maximum mass of neutron stars and strange neutron-star cores
- Incompressibility in finite nuclei and nuclear matter
- Results from the Relativistic Heavy Ion Collider
- Spectral Representations of Neutron-Star Equations of State
- Jet Flavor Classification in High-Energy Physics with Deep Neural Networks
- Analysis of the Quiescent Low-Mass X-ray Binary Population in Galactic Globular Clusters
- The Thermal X-ray Spectra of Cen X-4, Aql X-1, and 4U 1608-522 in Quiescence
- Kaon Condensation in Neutron Star Matter with Hyperons
- New Constraints on Neutron Star Cooling from Chandra Observations of 3C58
- Quark deconfinement in high-mass neutron stars
- Competition between delta isobars and hyperons and properties of compact stars
- Mapping neutron star data to the equation of state using the deep neural network
- Atmospheres and radiating surfaces of neutron stars
- Neutron Star Mass-Radius Constraints of the Quiescent Low-mass X-ray Binaries X7 and X5 in the Globular Cluster 47 Tuc
- Causal Representations of Neutron-Star Equations of State
- Cooling of Accretion-Heated Neutron Stars
- Spectral Approach to the Relativistic Inverse Stellar Structure Problem II
- Hot quark matter and (proto-) neutron stars
- 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
- Uncertainty Aware Learning for High Energy Physics
- SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention
- Unveiling the nuclear matter EoS from neutron star properties: a supervised machine learning approach
- Neutron star structure from QCD
- Learning to Simulate High Energy Particle Collisions from Unlabeled Data
- Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks
- A unified binary neutron star merger magnetar model for the Chandra X-ray transients CDF-S XT1 and XT2
- Towards a Deep Learning Model for Hadronization
- The impact of surface temperature inhomogeneities on quiescent neutron star radius measurements
- Hyperonic Neutron Star Matter in Light of GW170817
- Learning Optimal Test Statistics in the Presence of Nuisance Parameters
Cited by in corpus (9)
- 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
- Implications of latest NICER data for the neutron star equation of state
- 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
- Mass and tidal parameter extraction from gravitational waves of binary neutron stars mergers using deep learning
- Nuclear mass table in density functional approach inspired by neutron-star observations
- Extracting latent representations from X-ray spectra. Classification, regression, and accretion signatures of Chandra sources