An Early-warning System for Electromagnetic Follow-up of Gravitational-wave Events

Creators: Sachdev, Surabhi; Magee, Ryan; Hanna, Chad; Cannon, Kipp; Singer, Leo; Rana SK, Javed; Mukherjee, Debnandini; Caudill, Sarah; Chan, Chiwai; Creighton, Jolien D. E.; Ewing, Becca; Fong, Heather; Godwin, Patrick; Huxford, Rachael; Kapadia, Shasvath; Li, Alvin K. Y.; Lo, Rico Ka Lok; Meacher, Duncan; Messick, Cody; Mohite, Siddharth R.; Nishizawa, Atsushi; Ohta, Hiroaki; Pace, Alexander; Reza, Amit; Sathyaprakash, B. S.; Shikauchi, Minori; Singh, Divya; Tsukada, Leo; Tsuna, Daichi; Tsutsui, Takuya; Ueno, Koh

Abstract

Binary neutron stars (BNSs) will spend ≃10–15 minutes in the band of Advanced Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo detectors at design sensitivity. Matched-filtering of gravitational-wave (GW) data could in principle accumulate enough signal-to-noise ratio (S/N) to identify a forthcoming event tens of seconds before the companions collide and merge. Here we report on the design and testing of an early-warning GW detection pipeline. Early-warning alerts can be produced for sources that are at low enough redshift so that a large enough S/N accumulates ~10–60 s before merger. We find that about 7% (49%) of the total detectable BNS mergers will be detected 60 s (10 s) before the merger. About 2% of the total detectable BNS mergers will be detected before merger and localized to within 100 deg² (90% credible interval). Coordinated observing by several wide-field telescopes could capture the event seconds before or after the merger. LIGO–Virgo detectors at design sensitivity could facilitate observing at least one event at the onset of merger.

Additional Information

© 2020. The American Astronomical Society. Received 2020 September 8; revised 2020 November 2; accepted 2020 November 4; published 2020 December 21. This work was supported by the NSF grant OAC-1841480. S.S. is supported by the Eberly Research Funds of Penn State, The Pennsylvania State University, University Park, Pennsylvania. D.M. acknowledges the support of NSF PHY-1454389, ACI-1642391, and OAC-1841480. S.R.M. thanks the LSSTC Data Science Fellowship Program, which is funded by LSSTC, NSF Cybertraining grant #1829740, the Brinson Foundation, and the Moore Foundation; his participation in the program has benefited this work. B.S.S. is supported in part by NSF grant No. PHY-1836779 and AST-1708146. Computations for this research were performed on the Pennsylvania State Universitys Institute for Computational and Data Sciences Advanced CyberInfrastructure (ICDS-ACI). We also thank the LIGO Laboratory for use of its computing facility to make this work possible. The research leading to these results has also received funding from the European Union's Horizon 2020 Programme under the AHEAD2020 project (grant agreement No. 871158). Data: The data that were used to infer the results in this Letter are described in https://gstlal.docs.ligo.org/ewgw-data-release (Sachdev et al. 2020). Software: The analysis of the data and the detections of the simulation signals were made using the GstLAL-based inspiral software pipeline (Cannon et al. 2012; Privitera et al. 2014; Messick et al. 2017; Sachdev et al. 2019; Hanna et al. 2020). These are built on the LALSuite software library (LIGO Scientific Collaboration 2018). The sky localizations made use of ligo.skymap15 , which uses Astropy,16 a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013; Price-Whelan et al. 2018). The plots were prepared using Matplotlib (Hunter 2007).

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