Multiwaveform inference of gravitational waves

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.