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We reconstruct posterior distributions for the position (sky area anddistance) of a simulated set of binary neutron-star gravitational-waves signalsobserved with Advanced LIGO and Advanced Virgo. We use a Dirichlet ProcessGaussian-mixture model, a fully Bayesian non-parametric method that can be usedto estimate probability density functions with a flexible set of assumptions.The ability to reliably reconstruct the source position is important formultimessenger astronomy, as recently demonstrated with GW170817. We show thatfor detector networks comparable to the early operation of Advanced LIGO andAdvanced Virgo, typical localization volumes are$\sim10^4$--$10^5~\mathrm{Mpc^3}$ corresponding to $\sim10^2$--$10^3$ potentialhost galaxies. The localization volume is a strong function of the networksignal-to-noise ratio, scaling roughly $\propto \varrho_{net}^{-6}$. Fractionallocalizations improve with the addition of further detectors to the network.Our Dirichlet Process Gaussian-mixture model can be adopted for localizingevents detected during future gravitational-wave observing runs, and used tofacilitate prompt multimessenger follow-up.

Dirichlet Process Gaussian-mixture model: An application to localizing coalescing binary neutron stars with gravitational-wave observations