Normalizing flows with multibanding, heterodyning, and neural compression can produce BNS parameter posteriors and EOS constraints for 3G-detector signals in about a second, with accuracy restored by importance sampling.
Semianalytical Approach for Sky Localization of Gravitational Waves
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abstract
Rapid sky localization of gravitational wave sources is crucial to enable prompt electromagnetic follow-ups. In this article, we present a novel semianalytical approach for sky localization of gravitational waves from compact binary coalescences. We use the Bayesian framework with an analytical approximation to the prior distributions for a given astrophysical model. We derive a semianalytical solution to the posterior distribution of source directions. This method only requires one-fold numerical integral that marginalizes over the merger time, compared to the five-fold numerical integration otherwise needed in the Bayesian localization method. The performance of the method is demonstrated using a set of binary neutron stars (BNS) injections on Gaussian noise using LIGO-Virgo's design and O2 sensitivity. We find the median of 90% confidence area in O2 sensitivity to be $\mathcal{O}(10^2) ~\mathrm{deg}^2$, comparable to that of the existing LIGO-Virgo online localization method Bayestar and parameter estimation toolkit LALInference. In the end, we apply this method to localize the BNS event GW170817 and find the 50% (90%) confidence region of 11 $\mathrm{deg}^2$ (50 $\mathrm{deg}^2$). The detected optical counterpart of GW170817 resides within our 50% confidence area.
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gr-qc 1years
2024 1verdicts
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Decoding Long-duration Gravitational Waves from Binary Neutron Stars with Machine Learning: Parameter Estimation and Equations of State
Normalizing flows with multibanding, heterodyning, and neural compression can produce BNS parameter posteriors and EOS constraints for 3G-detector signals in about a second, with accuracy restored by importance sampling.