Year: 2020
Type: article
Abstract: As Einstein's equations for binary compact object inspiral have only been approximately or intermittently solved by analytic or numerical methods, the models used to infer parameters of gravitational wave (GW) sources are subject to waveform modeling uncertainty. Using a simple scenario, we illustrate these differences then introduce a very efficient technique to marginalize over waveform uncertainties, relative to a prespecified sequence of waveform models. Being based on RIFT, a very efficient parameter inference engine, our technique can directly account for any available models, including very accurate but computationally costly waveforms. Our evidence- and likelihood-based method works robustly on a point-by-point basis, enabling accurate marginalization for models with strongly disjoint posteriors while simultaneously increasing the reusability and efficiency of our intermediate calculations.
Institutions: Rochester Institute of Technology, Brown University
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
Awards: The Operation and Maintenance of the Laser Interferometer Gravitational Wave Observatory (LIGO), The Construction of the Advanced Laser Interferometer Gravitational Wave Observatory (AdvLIGO), WoU-MMA: Collaborative Research: Constraining the Nuclear Equation of State and Neutron Star Astrophysics Through Multi-messenger and Multi-object Observations of Neutron Stars, Gravitational Waves as a Tool of Astronomical Discovery, WoU-MMA: Inference about Gravitational-Wave Sources and Source Populations
OpenAlex ID: W3102200229