Bayesian analysis of the dynamic cosmic web in the SDSS galaxy survey
  Recent application of the Bayesian algorithm BORG to the Sloan Digital Sky
Survey (SDSS) main sample galaxies resulted in the physical inference of the
formation history of the observed large-scale structure from its origin to the
present epoch. In this work, we use these inferences as inputs for a detailed
probabilistic cosmic web-type analysis. To do so, we generate a large set of
data-constrained realizations of the large-scale structure using a fast, fully
non-linear gravitational model. We then perform a dynamic classification of the
cosmic web into four distinct components (voids, sheets, filaments, and
clusters) on the basis of the tidal field. Our inference framework
automatically and self-consistently propagates typical observational
uncertainties to web-type classification. As a result, this study produces
accurate cosmographic classification of large-scale structure elements in the
SDSS volume. By also providing the history of these structure maps, the
approach allows an analysis of the origin and growth of the early traces of the
cosmic web present in the initial density field and of the evolution of global
quantities such as the volume and mass filling fractions of different
structures. For the problem of web-type classification, the results described
in this work constitute the first connection between theory and observations at
non-linear scales including a physical model of structure formation and the
demonstrated capability of uncertainty quantification. A connection between
cosmology and information theory using real data also naturally emerges from
our probabilistic approach. Our results constitute quantitative
chrono-cosmography of the complex web-like patterns underlying the observed
galaxy distribution.
