Detection of GW190814 from the coalescence of a 23 solar-mass black hole and a 2.6 solar-mass compact object, the most unequal-mass binary yet observed with gravitational waves.
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A semi-supervised VAE trained on Skyrme EOS data reconstructs equations of state with mean absolute percentage errors under 0.14% using two supervised observables (M_max, R_1.4) and one variational latent variable.
Causal convolutional neural networks reconstruct neutron star observables for static, Keplerian, and rotating configurations in about 50 milliseconds per equation of state, compared to 30 minutes with traditional RNS calculations.
A double-polytropic EOS family is identified that satisfies multimessenger constraints with M_max around 2.45 solar masses, R_1.4 of 11.3 km, and Lambda_1.4 between 485-512 while remaining causal.
citing papers explorer
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GW190814: Gravitational Waves from the Coalescence of a 23 M$_\odot$ Black Hole with a 2.6 M$_\odot$ Compact Object
Detection of GW190814 from the coalescence of a 23 solar-mass black hole and a 2.6 solar-mass compact object, the most unequal-mass binary yet observed with gravitational waves.
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A Semi-Supervised Variational Autoencoder for Generating Neutron Star Equations of State
A semi-supervised VAE trained on Skyrme EOS data reconstructs equations of state with mean absolute percentage errors under 0.14% using two supervised observables (M_max, R_1.4) and one variational latent variable.
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Reconstruction of fast-rotating neutron star observables with the neural network
Causal convolutional neural networks reconstruct neutron star observables for static, Keplerian, and rotating configurations in about 50 milliseconds per equation of state, compared to 30 minutes with traditional RNS calculations.
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An Analytical Toy Equation of State for Neutron Stars Consistent with Current Observations
A double-polytropic EOS family is identified that satisfies multimessenger constraints with M_max around 2.45 solar masses, R_1.4 of 11.3 km, and Lambda_1.4 between 485-512 while remaining causal.