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

W. Del Pozzo, C. P. L. Berry, Archisman Ghosh, Tom S. F. Haines, A. Vecchio

Research output: Contribution to journalArticle

13 Citations (Scopus)
27 Downloads (Pure)

Abstract

We reconstruct posterior distributions for the position (sky area and distance) of a simulated set of binary neutron star gravitational-waves signals observed with Advanced LIGO and Advanced Virgo. We use a Dirichlet process Gaussian-mixture 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 signal-to-noise ratio, scaling roughly α ρ(variant) -6 net. Fractional localizations improve with the addition of further detectors to the network. Our Dirichlet process Gaussian-mixture model can be adopted for localizing events detected during future gravitational-wave observing runs and used to facilitate prompt multimessenger follow-up.

Original languageEnglish
Pages (from-to)601-614
Number of pages14
JournalMonthly Notices of the Royal Astronomical Society
Volume479
Issue number1
Early online date6 Jun 2018
DOIs
Publication statusPublished - 1 Sep 2018

Keywords

  • Gamma-ray burst: general
  • Gravitational waves
  • Methods: data analysis
  • Methods: statistical
  • Stars: neutron

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science

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