Work

Multiwaveform inference of gravitational waves

Year: 2020

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.

Source: Physical review. D/Physical review. D.

DOI: 10.1103/physrevd.101.064037

Authors: Gregory Ashton, Sebastian Khan

Institutions: ARC Centre of Excellence for Gravitational Wave Discovery, Monash University, Leibniz University Hannover, Max Planck Institute for Gravitational Physics, Cardiff University

Language: English

Topic: Pulsars and Gravitational Waves Research

Subfield: Astronomy and Astrophysics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Centre National de la Recherche Scientifique, Australian Research Council

Awards: Putting Einstein to the test: Probing gravity with gravitational waves, The Operation and Maintenance of the Laser Interferometer Gravitational Wave Observatory (LIGO), The Construction of the Advanced Laser Interferometer Gravitational Wave Observatory (AdvLIGO), Gravitational-wave astronomy: detection and beyond, ARC Centre of Excellence for Gravitational Wave Discovery