A rigorous EFT-based forward model for large-scale structure
  Conventional approaches to cosmology inference from galaxy redshift surveys
are based on n-point functions, which are under rigorous perturbative control
on sufficiently large scales. Here, we present an alternative approach, which
employs a likelihood at the level of the galaxy density field. By integrating
out small-scale modes based on effective-field theory arguments, we prove that
this likelihood is under perturbative control if certain specific conditions
are met. We further show that the information captured by this likelihood is
equivalent to the combination of the next-to-leading order galaxy power
spectrum, leading-order bispectrum, and BAO reconstruction. Combined with MCMC
sampling and MAP optimization techniques, our results allow for fully Bayesian
cosmology inference from large-scale structure that is under perturbative
control. We illustrate this via a first demonstration of unbiased cosmology
inference from nonlinear large-scale structure using this likelihood. In
particular, we show unbiased estimates of the power spectrum normalization
$\sigma_{8}$ from a catalog of simulated dark matter halos, where nonlinear
information is crucial in breaking the $b_{1} - \sigma_{8}$ degeneracy.
