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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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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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.