@article{70212611110444688f30e03603673eec,
title = "Dirichlet process Gaussian-mixture model: An application to localizing coalescing binary neutron stars with gravitational-wave observations",
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 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.",
keywords = "Gamma-ray burst: general, Gravitational waves, Methods: data analysis, Methods: statistical, Stars: neutron",
author = "{Del Pozzo}, W. and Berry, {Christopher Philip Luke} and A. Ghosh and Haines, {T. S.F.} and Singer, {L. P.} and A. Vecchio",
note = "Funding Information: The authors are grateful for useful suggestions from the CBC group of the LIGO Scientific and Virgo Collaborations; WDP thanks Neil Cornish, Tjonnie Li, Trevor Sidery, and John Veitch for early suggestions and discussions. We thank Will Farr for discussions on localization algorithms, and thank Ilya Mandel, Jonathan Gair, Hannah Middleton, Ewan Cameron, and the anonymous referee for comments on the manuscript. We thank the other authors of Singer et al. (2014) and Berry et al. (2015) for sharing the data for this work. We also would like to thank contributors of GLADE for making the catalogue publicly available, and especially Gergely D{\'a}lya for help with its documentation and use. This work was supported in part by Leverhulme Trust research project grant and in part by the Science and Technology Facilities Council. LIGO was constructed by the California Institute of Technology and Massachusetts Institute of Technology with funding from the National Science Foundation and operates under cooperative agreement PHY-0757058. This work used computing resources of the LIGO Data Grid including the Atlas computing cluster at the Albert Einstein Institute, Hannover; the LIGO computing clusters at Caltech, and the facilities of the Advanced Research Computing @ Cardiff (ARCCA) Cluster at Cardiff University. We are grateful for computational resources provided by the Leonard E Parker Center for Gravitation, Cosmology, and Astrophysics at University of Wisconsin-Milwaukee. Some results were produced using the post-processing tools of the plotutils library at github.com/farr/plotutils and skyarea library at github .com/farr/skyarea. The Dirichlet process Gaussian-mixture Model is included as a module available from github.com/thaines/helit/ and our implementation for three-dimensional localization is available from github.com/wdpozzo/3d volume. We thank GW150914, GW170104, and GW170817 for delaying the completion of this work. Publisher Copyright: {\textcopyright} 2018 The Author(s). Published by Oxford University Press on behalf of The Royal Astronomical Society.",
year = "2018",
month = sep,
day = "1",
doi = "10.1093/mnras/sty1485",
language = "English (US)",
volume = "479",
pages = "601--614",
journal = "Monthly Notices of the Royal Astronomical Society",
issn = "0035-8711",
publisher = "Oxford University Press",
number = "1",
}