Modeling noise in gravitational-wave observatories with transdimensional models

Modeling noise in gravitational-wave observatories is crucial for accurately inferring the properties of gravitational-wave sources. We introduce a transdimensional Bayesian approach to characterize the noise in ground-based gravitational-wave observatories using the Bayesian inference software bilby. The algorithm models broadband noise with a combination of power laws; narrow-band features with Lorentzians; and shapelets to capture any additional features in the data. We show that our noise model provides a significantly improved fit of the Laser Interferometer Gravitational-Wave Observatory and Virgo noise amplitude spectral densities compared to currently available noise fits obtained with on-source data segments. We perform astrophysical inference on well-known events in the third Gravitational-Wave Transient Catalog using our noise model and observe shifts of up to 7% in the 90% boundaries of credible intervals for some parameters. We discuss plans to deploy this framework systematically for gravitational-wave inference along with possible areas of improvement.