Inferring Kilonova Population Properties with a Hierarchical Bayesian Framework I : Non-Detection Methodology and Single-Event Analyses
Draft version July 16, 2021 Typeset using LATEX twocolumn style in AASTeX631 Inferring kilonova population properties with a hierarchical Bayesian framework I : Non-detection methodology and single-event analyses Siddharth R. Mohite,1, 2, ∗ Priyadarshini Rajkumar,3 Shreya Anand,4 David L. Kaplan,1 Michael W. Coughlin,5 Ana Sagues-Carracedo´ ,6 Muhammed Saleem,5 Jolien Creighton,1 Patrick R. Brady,1 Tomas´ Ahumada,7 Mouza Almualla,8 Igor Andreoni,4 Mattia Bulla,9 Matthew J. Graham,4 Mansi M. Kasliwal,4 Stephen Kaye,10 Russ R. Laher,11 Kyung Min Shin,12 David L. Shupe,11 and Leo P. Singer13, 14 1Center for Gravitation, Cosmology and Astrophysics, Department of Physics, University of Wisconsin{Milwaukee, P.O. Box 413, Milwaukee, WI 53201, USA 2Center for Computational Astrophysics, Flatiron Institute, 162 5th Ave, New York, NY 10010, USA 3Department of Physics and Astronomy, Texas Tech University, Lubbock, TX 79409-1051, USA 4Division of Physics, Mathematics and Astronomy, California Institute of Technology, Pasadena, CA 91125, USA 5School of Physics and Astronomy, University of Minnesota, Minneapolis, Minnesota 55455, USA 6The Oskar Klein Centre, Department of Physics, Stockholm University, AlbaNova, SE-106 91 Stockholm, Sweden 7Department of Astronomy, University of Maryland, College Park, MD 20742, USA 8Department of Physics, American University of Sharjah, PO Box 26666, Sharjah, UAE 9The Oskar Klein Centre, Department of Astronomy, Stockholm University, AlbaNova, SE-10691 Stockholm, Sweden 10Caltech Optical Observatories, California
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