Maximum-Likelihood Comparisons of Tully-Fisher and Redshift Data:   Constraints on Omega and Biasing
  We compare Tully-Fisher (TF) data for 838 galaxies within cz=3000 km/sec from the Mark III catalog to the peculiar velocity and density fields predicted from the 1.2 Jy IRAS redshift survey. Our goal is to test the relation between the galaxy density and velocity fields predicted by gravitational instability theory and linear biasing, and thereby to estimate $\beta_I = \Omega^{0.6}/b_I,$ where $b_I$ is the linear bias parameter for IRAS galaxies. Adopting the IRAS velocity and density fields as a prior model, we maximize the likelihood of the raw TF observables, taking into account the full range of selection effects and properly treating triple-valued zones in the redshift-distance relation. Extensive tests with realistic simulated galaxy catalogs demonstrate that the method produces unbiased estimates of $\beta_I$ and its error. When we apply the method to the real data, we model the presence of a small but significant velocity quadrupole residual (~3.3% of Hubble flow), which we argue is due to density fluctuations incompletely sampled by IRAS. The method then yields a maximum likelihood estimate $\beta_I=0.49\pm 0.07$ (1-sigma error). We discuss the constraints on $\Omega$ and biasing that follow if we assume a COBE-normalized CDM power spectrum. Our model also yields the 1-D noise noise in the velocity field, including IRAS prediction errors, which we find to be be 125 +/- 20 km/sec. 
Jeffrey A. Willick (Stanford University), Michael A. Strauss   (Princeton University), Avishai Dekel (Hebrew University), an...