TY - JOUR
T1 - Rapid Localization of Gravitational Wave Sources from Compact Binary Coalescences Using Deep Learning
AU - Chatterjee, Chayan
AU - Kovalam, Manoj
AU - Wen, Linqing
AU - Beveridge, Damon
AU - Diakogiannis, Foivos
AU - Vinsen, Kevin
N1 - Funding Information:
This research was supported in part by the Australian Research Council Centre of Excellence for Gravitational Wave Discovery (OzGrav, through Project No. CE170100004). This research was undertaken with the support of computational resources from the Pople high-performance computing cluster of the Faculty of Science at the University of Western Australia. This work used the computer resources of the OzStar computer cluster at Swinburne University of Technology. The OzSTAR program receives funding in part from the Astronomy National Collaborative Research Infrastructure Strategy (NCRIS) allocation provided by the Australian Government. This research used data obtained from the Gravitational Wave Open Science Center (https://www.gw-openscience.org), a service of LIGO Laboratory, the LIGO Scientific Collaboration, and the Virgo Collaboration. LIGO is funded by the U.S. National Science Foundation. Virgo is funded by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale della Fisica Nucleare (INFN), and the Dutch Nikhef, with contributions by Polish and Hungarian institutes. This material is based upon work supported by NSF’s LIGO Laboratory which is a major facility fully funded by the National Science Foundation. The authors would like to thank Prof. Amitava Datta from The University of Western Australia for help in this work.
Funding Information:
This research was supported in part by the Australian Research Council Centre of Excellence for Gravitational Wave Discovery (OzGrav, through Project No. CE170100004). This research was undertaken with the support of computational resources from the Pople high-performance computing cluster of the Faculty of Science at the University of Western Australia. This work used the computer resources of the OzStar computer cluster at Swinburne University of Technology. The OzSTAR program receives funding in part from the Astronomy National Collaborative Research Infrastructure Strategy (NCRIS) allocation provided by the Australian Government. This research used data obtained from the Gravitational Wave Open Science Center ( https://www.gw-openscience.org ), a service of LIGO Laboratory, the LIGO Scientific Collaboration, and the Virgo Collaboration. LIGO is funded by the U.S. National Science Foundation. Virgo is funded by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale della Fisica Nucleare (INFN), and the Dutch Nikhef, with contributions by Polish and Hungarian institutes. This material is based upon work supported by NSF’s LIGO Laboratory which is a major facility fully funded by the National Science Foundation. The authors would like to thank Prof. Amitava Datta from The University of Western Australia for help in this work.
Publisher Copyright:
© 2023. The Author(s). Published by the American Astronomical Society.
PY - 2023/12/10
Y1 - 2023/12/10
N2 - The mergers of neutron star-neutron star and neutron star-black hole binaries (NSBHs) are the most promising gravitational wave (GW) events with electromagnetic (EM) counterparts. The rapid detection, localization, and simultaneous multimessenger follow-up of these sources are of primary importance in the upcoming science runs of the LIGO-Virgo-KAGRA Collaboration. While prompt EM counterparts during binary mergers can last less than 2 s, the timescales of existing localization methods that use Bayesian techniques, vary from seconds to days. In this paper, we propose the first deep learning-based approach for rapid and accurate sky localization of all types of binary coalescences, including neutron star-neutron star and NSBHs for the first time. Specifically, we train and test a normalizing flow model on matched-filtering output from GW searches to obtain sky direction posteriors in around 1 s using a single P100 GPU, which is several orders of magnitude faster than full Bayesian techniques.
AB - The mergers of neutron star-neutron star and neutron star-black hole binaries (NSBHs) are the most promising gravitational wave (GW) events with electromagnetic (EM) counterparts. The rapid detection, localization, and simultaneous multimessenger follow-up of these sources are of primary importance in the upcoming science runs of the LIGO-Virgo-KAGRA Collaboration. While prompt EM counterparts during binary mergers can last less than 2 s, the timescales of existing localization methods that use Bayesian techniques, vary from seconds to days. In this paper, we propose the first deep learning-based approach for rapid and accurate sky localization of all types of binary coalescences, including neutron star-neutron star and NSBHs for the first time. Specifically, we train and test a normalizing flow model on matched-filtering output from GW searches to obtain sky direction posteriors in around 1 s using a single P100 GPU, which is several orders of magnitude faster than full Bayesian techniques.
UR - https://www.scopus.com/pages/publications/85180081903
U2 - 10.3847/1538-4357/ad08b7
DO - 10.3847/1538-4357/ad08b7
M3 - Article
AN - SCOPUS:85180081903
SN - 0004-637X
VL - 959
JO - Astrophysical Journal
JF - Astrophysical Journal
IS - 1
M1 - 42
ER -