Parameter estimation of binary black hole systems using numerical relativity surrogates and a rapid inference framework

Extraction of astrophysical information from gravitational wave (GW) observations relies on both accurate models and rapid analysis algorithms. For high-mass binary black hole systems, one of the most abundant sources of GWs so far, the most accurate waveform templates are generated by numerical relativity simulations which may take weeks to finish for just a single simulation. Surrogate modeling techniques provide an interesting solution to address these issues and are now being integrated into parameter estimation codes. This talk will discuss the integration of a newly developed aligned-spin surrogate waveform model along with the highly parallelizable inference algorithm RIFT (Rapid parameter inference on gravitational wave sources via Iterative FitTing) that can perform an efficient evaluation of the computationally expensive likelihood function. Preliminary parameter estimation results from this project for high-spin systems will be briefly discussed.