Published May 2022 | Published + Submitted
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Target-of-opportunity Observations of Gravitational-wave Events with Vera C. Rubin Observatory

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Abstract

The discovery of the electromagnetic counterpart to the binary neutron star (NS) merger GW170817 has opened the era of gravitational-wave multimessenger astronomy. Rapid identification of the optical/infrared kilonova enabled a precise localization of the source, which paved the way to deep multiwavelength follow-up and its myriad of related science results. Fully exploiting this new territory of exploration requires the acquisition of electromagnetic data from samples of NS mergers and other gravitational-wave sources. After GW170817, the frontier is now to map the diversity of kilonova properties and provide more stringent constraints on the Hubble constant, and enable new tests of fundamental physics. The Vera C. Rubin Observatory's Legacy Survey of Space and Time can play a key role in this field in the 2020s, when an improved network of gravitational-wave detectors is expected to reach a sensitivity that will enable the discovery of a high rate of merger events involving NSs (∼tens per year) out to distances of several hundred megaparsecs. We design comprehensive target-of-opportunity observing strategies for follow-up of gravitational-wave triggers that will make the Rubin Observatory the premier instrument for discovery and early characterization of NS and other compact-object mergers, and yet unknown classes of gravitational-wave events.

Additional Information

© 2022. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Received 2021 November 3; revised 2022 March 3; accepted 2022 March 24; published 2022 May 13. We thank Lynne Jones for her work on LSST strategy simulations. This paper was created in the nursery of the Rubin LSST Transient and Variable Star Science Collaboration. 53 The authors acknowledge the support of the Vera C. Rubin Legacy Survey of Space and Time Transient and Variable Stars Science Collaboration, which provided opportunities for collaboration and exchange of ideas and knowledge, and of Rubin Observatory in the creation and implementation of this work. The authors acknowledge the support of the LSST Corporation, which enabled the organization of many workshops and hackathons throughout the cadence optimization process by directing private funding to these activities. R.M. acknowledges support from the National Science Foundation under grant No. AST-1909796 and AST-1944985, and by the Heising-Simons foundation. M.W.C. acknowledges support from the National Science Foundation with grant Nos. PHY-2010970 and OAC-2117997. A.C. acknowledges support from the NSF award AST #1907975. S.J.S. acknowledges funding from STFC grants ST/T000198/1 and ST/S006109/1. D.M. acknowledges NSF support from grants PHY-1914448 and AST-2037297 K.M. acknowledges support from EU H2020 ERC grant No. 758638. A.H. is partially supported by a Future Investigators in NASA Earth and Space Science and Technology (FINESST) award No. 80NSSC19K1422. M.N. acknowledges support from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No. 948381) and a Fellowship from the Alan Turing Institute. P.D.'A. acknowledges support from PRIN-MIUR 2017 (grant 20179ZF5KS) and from the Italian Space Agency, contract ASI/INAF No. I/004/11/4. E.C.K. and A.G. acknowledge support from the G.R.E.A.T research environment funded by Vetenskapsr å det, the Swedish Research Council, under project No. 2016-06012, and support from The Wenner-Gren Foundations. M.B. acknowledges support from the Swedish Research Council (Reg. No. 2020-03330). The UCSC team is supported in part by NASA grant NNG17PX03C, NSF grant AST-1815935, the Gordon & Betty Moore Foundation, the Heising-Simons Foundation, and by a fellowship from the David and Lucile Packard Foundation to R.J.F. This work was supported by the Preparing for Astrophysics with LSST Program, funded by the Heising Simons Foundation through grant 2021-2975, and administered by Las Cumbres Observatory. Software: LSST metrics analysis framework (MAF; Jones et al. 2014); astropy (Astropy Collaboration et al. 2013); matplotlib; ligo.skymap. 54

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Created:
August 22, 2023
Modified:
October 23, 2023