Mass ratio reversals produce qualitatively different contributions to BBH merger rates and masses in COMPAS versus SEVN simulations, with core-growth dominating and most systems arising from massive low-metallicity progenitors.
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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.
Different parametrizations of density dependence in covariant density functionals produce significant variations in the high-density equation of state and symmetry energy, with rational-function forms providing flexibility when saturation properties are adjusted and constrained by multimessenger ast
citing papers explorer
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Massquerade: Impacts of Mass Ratio Reversals on Binary Black Hole Merger Rates and Mass Distributions
Mass ratio reversals produce qualitatively different contributions to BBH merger rates and masses in COMPAS versus SEVN simulations, with core-growth dominating and most systems arising from massive low-metallicity progenitors.
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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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Bayesian inferences on covariant density functionals from multimessenger astrophysical data: Influences of parametrizations of density dependent couplings
Different parametrizations of density dependence in covariant density functionals produce significant variations in the high-density equation of state and symmetry energy, with rational-function forms providing flexibility when saturation properties are adjusted and constrained by multimessenger ast