Multiwaveform inference of gravitational waves

Ashton, Gregory and Khan, Sebastian (2020) Multiwaveform inference of gravitational waves. Physical Review D, 101 (6). (doi:10.1103/PhysRevD.101.064037).

Record type: Article

Abstract

Bayesian inference of gravitational wave signals is subject to systematic error due to modeling uncertainty in waveform signal models coined approximants. A growing collection of approximants are available which use different approaches and make different assumptions to ease the process of model development. We provide a method to marginalize over the uncertainty in a set of waveform approximants by constructing a mixture-model multiwaveform likelihood. This method fits into existing workflows by determining the mixture parameters from the per-waveform evidence, enabling the production of marginalized combined sample sets from independent runs.

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Published date: 18 March 2020

Additional Information: Funding Information: We thank Christopher Berry, John Veitch, Michael Pürrer, and members of the LIGO and Virgo Compact Binary Coalescence group for valuable input during the preparation of this manuscript. We also thank the anonymous referee for valuable comments which improved the manuscript during review. G. A. is supported by the Australian Research Council through Grants No. CE170100004, No. FT150100281, and No. DP180103155. S. K. acknowledges support by the Max Planck Society’s Independent Research Group Grant. This research has made use of data, software, and/or web tools obtained from the Gravitational Wave Open Science Center , a service of LIGO Laboratory, the LIGO Scientific Collaboration, and the Virgo Collaboration. LIGO is funded by the U.S. National Science Foundation. Virgo is funded by the French Centre National de Recherche Scientifique, the Italian Istituto Nazionale della Fisica Nucleare, and the Dutch Nikhef, with contributions by Polish and Hungarian institutes. The authors are grateful for computational resources provided by the LIGO Laboratory and supported by National Science Foundation Grants No. PHY-0757058 and No. PHY-0823459. All analyses performed with bilby in this work make use of the dynesty nested-sampling package. The scipy and matplotlib packages are used for statistical computations and creating figures. Publisher Copyright: © 2020 American Physical Society. M1 - 064037