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.
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 language | English |
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Pages (from-to) | 601-614 |
Journal | Monthly Notices of the Royal Astronomical Society |
Volume | 479 |
Issue number | 1 |
Early online date | 6 Jun 2018 |
DOIs | |
Publication status | Published - 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/sty1485Keywords
- astro-ph.IM
- gr-qc
- gravitational waves
- methods: data analysis
- methods: statistical
- gamma ray burst: general
- stars: neutron