NS-UNO: Neutron Star EoS Inference from an Unconstrained Number of Observations

arXiv:2608.30573 · nucl-th, astro-ph.HE, astro-ph.IM, hep-ph · Submitted 2026-08-31 · Read on arXiv

nucl-th, astro-ph.HE, astro-ph.IM, hep-ph

Submitted: 2026-08-31

Updated: 2026-08-31

Terminology

• Reconstruction Error — 16× in this paper · explained in 2 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Generalization — 8× in this paper · explained in 13 episodes
• Calibration — 7× in this paper · explained in 7 episodes
• Phase Transitions — 5× in this paper · explained in 2 episodes
• Empirical Coverage — 4× in this paper · explained in 2 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Normalizing Flow — 3× in this paper · explained in 3 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Conditional Normalizing Flow — 2× in this paper
• Consistency — 2× in this paper · explained in 3 episodes
• Coverage Calibration Error — 2× in this paper
• Equation of State (EoS) — 2× in this paper · explained in 4 episodes
• Normalizing Flows — 2× in this paper · explained in 4 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Gaussian Process — 1× in this paper
• Neural Posterior Estimation — 1× in this paper
• Neural posterior estimation (NPE) — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Tolman Oppenheimer Volkoff — 1× in this paper

Sources

• Neutron stars and the dense matter equation of state: from microscopic theory to macroscopic observations
• Properties of the binary neutron star merger GW170817
• GWTC-5.0: An Introduction to Version 5.0 of the Gravitational-Wave Transient Catalog
• Refined Mass and Geometric Measurements of the High-Mass PSR J0740+6620
• Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy
• Cosmic Explorer: The U.S. Contribution to Gravitational-Wave Astronomy beyond LIGO
• Real-time gravitational-wave inference for binary neutron stars using machine learning
• Amortized Simulation-Based Inference of Relativistic Mean-Field Couplings for Neutron-Star Equations of State
• Neutron Star Equation of State via Physics Informed Neural Network
• A Partially Supervised Latent-Space Variational Autoencoder for Generating Neutron Star Equations of State
• Uncertainty quantification in the machine-learning inference from neutron star probability distribution to the equation of state
• Deep Sets
• Learning Likelihoods with Conditional Normalizing Flows
• Composition and thermodynamics of nuclear matter with light clusters
• Core-collapse supernova equations of state based on neutron star observations
• A New Parameterization for the Lagrangian Density of Relativistic Mean Field Theory
• Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
• Adam: A Method for Stochastic Optimization

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