The EFT Likelihood for Large-Scale Structure in Redshift Space
  We study the EFT likelihood for biased tracers in redshift space, for which
the bias expansion of the galaxy velocity field $\mathbf{v}_g$ plays a
fundamental role. The equivalence principle forbids stochastic contributions to
$\mathbf{v}_g$ to survive at small $k$. Therefore, at leading order in
derivatives the form of the likelihood ${\cal
P}[\tilde{\delta}_g|\delta,\!\mathbf{v}]$ to observe a redshift-space galaxy
overdensity $\tilde{\delta}_g(\tilde{\mathbf{x}})$ given a rest-frame matter
and velocity fields $\delta(\mathbf{x})$, $\mathbf{v}(\mathbf{x})$ is fixed by
the rest-frame noise. If this noise is Gaussian with constant power spectrum,
${\cal P}[\tilde{\delta}_g|\delta,\!\mathbf{v}]$ is also a Gaussian in the
difference between $\tilde{\delta}_g(\tilde{\mathbf{x}})$ and its bias
expansion: redshift-space distortions only make the covariance depend on
$\delta(\mathbf{x})$ and $\mathbf{v}(\mathbf{x})$. We then show how to match
this result to perturbation theory, and that one can consistently neglect the
field-dependent covariance if the bias expansion is stopped at second order in
perturbations. We discuss qualitatively how this affects numerical
implementations of the EFT-based forward modeling, and how the picture changes
when the survey window function is taken into account.
