Dirichlet process Gaussianmixture model: An application to localizing coalescing binary neutron stars with gravitationalwave observations
Abstract
We reconstruct posterior distributions for the position (sky area and distance) of a simulated set of binary neutron star gravitationalwaves signals observed with Advanced LIGO and Advanced Virgo. We use a Dirichlet process Gaussianmixture model, a fully Bayesian nonparametric method that can be used to estimate probability density functions with a flexible set of assumptions. The ability to reliably reconstruct the source position is important for multimessenger astronomy, as recently demonstrated with GW170817. We show that for detector networks comparable to the early operation of Advanced LIGO and Advanced Virgo, typical localization volumes are ̃10^{4}10^{5} Mpc^{3} corresponding to ̃10^{2}10^{3} potential host galaxies. The localization volume is a strong function of the network signaltonoise ratio, scaling roughly ∝ ρ_net^{6}. Fractional localizations improve with the addition of further detectors to the network. Our Dirichlet process Gaussianmixture model can be adopted for localizing events detected during future gravitationalwave observing runs and used to facilitate prompt multimessenger followup.
 Publication:

Monthly Notices of the Royal Astronomical Society
 Pub Date:
 September 2018
 DOI:
 10.1093/mnras/sty1485
 arXiv:
 arXiv:1801.08009
 Bibcode:
 2018MNRAS.479..601D
 Keywords:

 gravitational waves;
 methods: data analysis;
 methods: statistical;
 gammaray burst: general;
 stars: neutron;
 Astrophysics  Instrumentation and Methods for Astrophysics;
 General Relativity and Quantum Cosmology
 EPrint:
 16 pages, 5 figures, accepted for publication on MNRAS