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tokens: to a redshift. The statistical uncertainty associated with estimation of the source-frame masses is increased relative to that of the detector-frame masses owing to these dependencies. From the inferred posterior distribution of D L we compute redshift assuming a Planck 2015 ΛCDM cosmology with Hubble parameter H 0 = 67.9 km s −1 Mpc −1 (Ade et al. 2016 ) (and we address the effect of taking a larger value of the Hubble parameter in Section 2.2 ). Unless stated otherwise, mass measurements are quoted in the source frame of the binary. 2.1. Method and Signal Models To infer the source properties of GW190521, we analyzed 8 s of data in the LIGO and Virgo detectors around the time of the detection. The data are downsampled from 16,384 to 1024 Hz, as we expect no signal power above several hundred Hz owing to the total mass of GW190521. The parameter estimation analysis is done with two independently developed coherent Bayesian inference pipelines, LALInference (Veitch et al. 2015 ) and RIFT (Lange et al. 2018 ; Wysocki et al. 2019 ), which produce consistent results for the inferred source parameters. Both parameter estimation algorithms assume stationary Gaussian noise characterized by the power spectral density (PSD), which is inferred from the data by the BayesLine algorithm (Littenberg Cornish 2015 ). We compute the event likelihood in the frequency domain, integrating over the frequency band 11–512 Hz. We used three distinct GW signal models of BBH coalescence in our analysis: NRSur7dq4 (NRSur PHM), a surrogate waveform model built by directly interpolating NR solutions (Varma et al. 2019 ); IMRPhenomPv3HM (Phenom PHM), an inspiral-merger-ringdown waveform model that uses phenomenological frequency domain fits combining post-Newtonian calculations of the GW phase and amplitude (Blanchet et al. 1995 , 2005 ; Damour et al. 2001 ; Arun et al. 2009 ; Blanchet 2014 ) with tuning to NR solutions (Khan et al. 2020 ); and SEOBNRv4PHM (SEOBNR PHM), an inspiral-merger-ringdown waveform model that is based on the effective-one-body formalism (Buonanno Damour 1999 , 2000 ) and calibrated to NR (Ossokine et al. 2020 ). These three waveform models employ different approaches to reproduce the predictions from analytical relativity and numerical relativity; we expect to see differences in the parameter estimation from these three models as a result of those different approaches, and we can interpret those differences as a form of systematic error associated with the modeling. Note that the effects of the astrophysical environment, such as the presence of gas, on the GW waveform are expected to be negligible (Fedrow et al. 2017 ) in the late stages of inspiral, merger, and ringdown that we observe. NRSur PHM is constructed based on NR simulations with component spins that are not constrained to be aligned with the orbital axis, thus including the effects of spin–orbit precession. The model covers dimensionless spin magnitudes χ i ≤ 0.8 and mass