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Published December 1, 2021 | Published + Submitted
Journal Article Open

The Challenges Ahead for Multimessenger Analyses of Gravitational Waves and Kilonova: A Case Study on GW190425

Abstract

In recent years, there have been significant advances in multimessenger astronomy due to the discovery of the first, and so far only confirmed, gravitational wave event with a simultaneous electromagnetic (EM) counterpart, as well as improvements in numerical simulations, gravitational wave (GW) detectors, and transient astronomy. This has led to the exciting possibility of performing joint analyses of the GW and EM data, providing additional constraints on fundamental properties of the binary progenitor and merger remnant. Here, we present a new Bayesian framework that allows inference of these properties, while taking into account the systematic modeling uncertainties that arise when mapping from GW binary progenitor properties to photometric light curves. We extend the relative binning method presented in Zackay et al. to include extrinsic GW parameters for fast analysis of the GW signal. The focus of our EM framework is on light curves arising from r-process nucleosynthesis in the ejected material during and after merger, the so-called kilonova, and particularly on black hole−neutron star systems. As a case study, we examine the recent detection of GW190425, where the primary object is consistent with being either a black hole or a neutron star. We show quantitatively how improved mapping between binary progenitor and outflow properties, and/or an increase in EM data quantity and quality are required in order to break degeneracies in the fundamental source parameters.

Additional Information

© 2021. The American Astronomical Society. Received 2021 February 23; revised 2021 August 26; accepted 2021 August 26; published 2021 December 6. We thank Kyohei Kawaguchi for providing us with their light curves and Andrew Williamson, Matthew Liska, Doosoo Yoon, Koushik Chatterjee, Philipp Moesta, Om Salafia, Masaomi Tanaka, and Kenta Kuichi for useful discussions. We thank Barbara Patricelli and Leo Singer for their careful reading of the paper and their comments. We are very grateful to the the LIGO Scientific, Virgo, and KAGRA Collaborations for public access to their data products of GW170817 and GW190425. We also thank the GROWTH collaboration for public access to their observational data products. G.R., S.M.N., T.H., and K.L. are grateful for financial support from the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) through the Projectruimte and VIDI grants (Nissanke). T.H. also acknowledges funding from the NWO sectorplan. F.F. and A.H. gratefully acknowledge support from NASA through grant number 80NSSC18K0565, from the DOE through Early Career Award DE-SC0020435, and from the NSF through grant No. PHY-1806278. M.M.K. acknowledges the GROWTH project funded by the National Science Foundation under PIRE Grant No 1545949. M.M.K. acknowledges generous support from the David and Lucille Packard Foundation. M.B. acknowledges support from the Swedish Research Council (Reg. No. 2020-03330). R.F. acknowledges support from the Natural Sciences and Engineering Research Council of Canada (NSERC) through Discovery Grant RGPIN-2017-04286, and from the Faculty of Science at the University of Alberta. T.V. acknowledges support by the John Bahcall Fellowship at the Institute for Advanced Study and by the National Science Foundation under grant No. 2012086. M.C. acknowledges support from the National Science Foundation with grant number PHY-2010970. T.E. acknowledges support by the Vetenskapsrådet (Swedish Research Council) through contract No. 638-2013-8993 and the Oskar Klein Centre for Cosmoparticle Physics. Software: Python/C language (Oliphant 2007), NumPy (van der Walt et al. 2011), Cython (Behnel et al. 2011), SciPy (Jones et al. 2001), MPI (Forum 1994), MPI for Python (Dalcín et al. 2008), Matplotlib (Hunter 2007; Droettboom et al. 2018), IPython (Perez & Granger 2007), Jupyter (Kluyver et al. 2016), MultiNest (Feroz et al. 2009), PyMultiNest (Buchner et al. 2014), GetDist (Lewis 2019).

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Published - Raaijmakers_2021_ApJ_922_269.pdf

Submitted - 2102.11569.pdf

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Additional details

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