COSMIC BIRTH: Efficient Bayesian Inference of the Evolving Cosmic Web
  from Galaxy Surveys
  We present COSMIC BIRTH: COSMological Initial Conditions from Bayesian
Inference Reconstructions with THeoretical models: an algorithm to reconstruct
the primordial and evolved cosmic density fields from galaxy surveys on the
light-cone. The displacement and peculiar velocity fields are obtained from
forward modelling at different redshift snapshots given some initial cosmic
density field within a Gibbs-sampling scheme. This allows us to map galaxies,
observed in a light-cone, to a single high redshift and hereby provide tracers
and the corresponding survey completeness in Lagrangian space including
phase-space mapping. These Lagrangian tracers in turn permit us to efficiently
obtain the primordial density field, making the COSMIC BIRTH code general to
any structure formation model. Our tests are restricted for the time being to
Augmented Lagrangian Perturbation theory. We show how to robustly compute the
non-linear Lagrangian bias from clustering measurements in a numerical way,
enabling us to get unbiased dark matter field reconstructions at initial cosmic
times. We also show that we can greatly recover the information of the dark
matter field from the galaxy distribution based on a detailed simulation. Novel
key ingredients to this approach are a higher-order Hamiltonian sampling
technique and a non-diagonal Hamiltonian mass-matrix. This technique could be
used to study the Eulerian galaxy bias from galaxy surveys and could become an
ideal baryon acoustic reconstruction technique. In summary, this method
represents a general reconstruction technique, including in a self-consistent
way a survey mask, non-linear and non-local bias and redshift space
distortions, with an efficiency about 10 times superior to previous comparable
methods.
