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

Walter Del Pozzo, Christopher Berry, Archisman Ghosh, Tom Haines, Leo Singer, Alberto Vecchio

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)
326 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 ∼104–105˜Mpc3 corresponding to ∼102–103 potential host galaxies. The localization volume is a strong function of the network signal-to-noise ratio, scaling roughly ∝ −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
JournalMonthly Notices of the Royal Astronomical Society
Volume479
Issue number1
Early online date6 Jun 2018
DOIs
Publication statusPublished - 1 Sept 2018

Bibliographical note

W Del Pozzo, C P L Berry, A Ghosh, T S F Haines, L P Singer, A Vecchio; Dirichlet process Gaussian-mixture model: An application to localizing coalescing binary neutron stars with gravitational-wave observations, Monthly Notices of the Royal Astronomical Society, Volume 479, Issue 1, 1 September 2018, Pages 601–614, https://doi.org/10.1093/mnras/sty1485

Keywords

  • astro-ph.IM
  • gr-qc
  • gravitational waves
  • methods: data analysis
  • methods: statistical
  • gamma ray burst: general
  • stars: neutron

Fingerprint

Dive into the research topics of 'Dirichlet process Gaussian-mixture model: an application to localizing coalescing binary neutron stars with gravitational-wave observations'. Together they form a unique fingerprint.

Cite this