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Inferring Kilonova Population Properties with a Hierarchical Bayesian Framework. I. Nondetection Methodology and Single-event Analyses

Mohite, Siddharth R. and Rajkumar, Priyadarshini and Anand, Shreya and Kaplan, David L. and Coughlin, Michael W. and Sagués-Carracedo, Ana and Saleem, Muhammed and Creighton, Jolien and Brady, Patrick R. and Ahumada, Tomás and Almualla, Mouza and Andreoni, Igor and Bulla, Mattia and Graham, Matthew J. and Kasliwal, Mansi M. and Kaye, Stephen and Laher, Russ R. and Shin, Kyung Min and Shupe, David L. and Singer, Leo P. (2022) Inferring Kilonova Population Properties with a Hierarchical Bayesian Framework. I. Nondetection Methodology and Single-event Analyses. Astrophysical Journal, 925 (1). Art. No. 58. ISSN 0004-637X. doi:10.3847/1538-4357/ac3981.

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We present nimbus: a hierarchical Bayesian framework to infer the intrinsic luminosity parameters of kilonovae (KNe) associated with gravitational-wave (GW) events, based purely on nondetections. This framework makes use of GW 3D distance information and electromagnetic upper limits from multiple surveys for multiple events and self-consistently accounts for the finite sky coverage and probability of astrophysical origin. The framework is agnostic to the brightness evolution assumed and can account for multiple electromagnetic passbands simultaneously. Our analyses highlight the importance of accounting for model selection effects, especially in the context of nondetections. We show our methodology using a simple, two-parameter linear brightness model, taking the follow-up of GW190425 with the Zwicky Transient Facility as a single-event test case for two different prior choices of model parameters: (i) uniform/uninformative priors and (ii) astrophysical priors based on surrogate models of Monte Carlo radiative-transfer simulations of KNe. We present results under the assumption that the KN is within the searched region to demonstrate functionality and the importance of prior choice. Our results show consistency with simsurvey—an astronomical survey simulation tool used previously in the literature to constrain the population of KNe. While our results based on uniform priors strongly constrain the parameter space, those based on astrophysical priors are largely uninformative, highlighting the need for deeper constraints. Future studies with multiple events having electromagnetic follow-up from multiple surveys should make it possible to constrain the KN population further.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Mohite, Siddharth R.0000-0003-1356-7156
Anand, Shreya0000-0003-3768-7515
Kaplan, David L.0000-0001-6295-2881
Coughlin, Michael W.0000-0002-8262-2924
Sagués-Carracedo, Ana0000-0002-3498-2167
Creighton, Jolien0000-0003-3600-2406
Brady, Patrick R.0000-0002-4611-9387
Ahumada, Tomás0000-0002-2184-6430
Almualla, Mouza0000-0002-4694-7123
Andreoni, Igor0000-0002-8977-1498
Bulla, Mattia0000-0002-8255-5127
Graham, Matthew J.0000-0002-3168-0139
Kasliwal, Mansi M.0000-0002-5619-4938
Laher, Russ R.0000-0003-2451-5482
Shin, Kyung Min0000-0002-1486-3582
Shupe, David L.0000-0003-4401-0430
Singer, Leo P.0000-0001-9898-5597
Alternate Title:Inferring kilonova population properties with a hierarchical Bayesian framework I : Non-detection methodology and single-event analyses
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 July 15; revised 2021 November 5; accepted 2021 November 12; published 2022 January 25. Based on observations obtained with the Samuel Oschin Telescope 48 inch and the 60 inch Telescope at the Palomar Observatory as part of the Zwicky Transient Facility project. ZTF is supported by the National Science Foundation under grant No. AST-1440341 and a collaboration including Caltech, IPAC, the Weizmann Institute for Science, the Oskar Klein Center at Stockholm University, the University of Maryland, the University of Washington, Deutsches Elektronen-Synchrotron and Humboldt University, Los Alamos National Laboratories, the TANGO Consortium of Taiwan, the University of Wisconsin at Milwaukee, and Lawrence Berkeley National Laboratories. Operations are conducted by COO, IPAC, and UW. This work was supported by the GROWTH (Global Relay of Observatories Watching Transients Happen) project funded by the National Science Foundation under PIRE grant No. 1545949. GROWTH is a collaborative project among the California Institute of Technology (USA), University of Maryland College Park (USA), University of Wisconsin-Milwaukee (USA), Texas Tech University (USA), San Diego State University (USA), University of Washington (USA), Los Alamos National Laboratory (USA), Tokyo Institute of Technology (Japan), National Central University (Taiwan), Indian Institute of Astrophysics (India), Indian Institute of Technology Bombay (India), Weizmann Institute of Science (Israel), The Oskar Klein Centre at Stockholm University (Sweden), Humboldt University (Germany), Liverpool John Moores University (UK), and University of Sydney (Australia). 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. S.R.M. and J.C. acknowledge support from the NSF grant NSF PHY #1912649. We are grateful for the computational resources provided by the Leonard E Parker Center for Gravitation, Cosmology and Astrophysics at the University of Wisconsin-Milwaukee. M.M.K. acknowledges generous support from the David and Lucille Packard Foundation. M.C. and M.S. acknowledge support from the National Science Foundation with grant number PHY-2010970. S.A. acknowledges support from the GROWTH PIRE grant No. 1545949. A.S.C acknowledges support from the G.R.E.A.T research environment, funded by Vetenskapsrådet, the Swedish Research Council, project number 2016-06012. M.B. acknowledges support from the Swedish Research Council (Reg. no. 2020-03330). We thank the reviewer whose comments and suggestions helped improve and clarify this manuscript. Facilities: LIGO - Laser Interferometer Gravitational-Wave Observatory, ZTF/PO:1.2m. - Software: ipython (Pérez & Granger 2007), jupyter (Kluyver et al. 2016), matplotlib (Hunter 2007), python (Van Rossum & Drake 2009), NumPy (Harris et al. 2020), scikit-learn (Pedregosa et al. 2011), scipy (Virtanen et al. 2020).
Group:Infrared Processing and Analysis Center (IPAC), Zwicky Transient Facility, Astronomy Department
Funding AgencyGrant Number
ZTF partner institutionsUNSPECIFIED
Large Synoptic Survey Telescope CorporationUNSPECIFIED
Brinson FoundationUNSPECIFIED
Gordon and Betty Moore FoundationUNSPECIFIED
David and Lucile Packard FoundationUNSPECIFIED
Swedish Research Council2016-06012
Swedish Research Council2020-03330
Subject Keywords:Bayesian statistics; Hierarchical models; Neutron stars; Gravitational wave sources; Surveys; Compact objects
Issue or Number:1
Classification Code:Unified Astronomy Thesaurus concepts: Bayesian statistics (1900); Hierarchical models (1925); Neutron stars (1108); Gravitational wave sources (677); Surveys (1671); Compact objects (288)
Record Number:CaltechAUTHORS:20211217-233205774
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Official Citation:Siddharth R. Mohite et al 2022 ApJ 925 58
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:112536
Deposited By: George Porter
Deposited On:20 Dec 2021 17:22
Last Modified:28 Jan 2022 19:09

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