Cosmological Constraints with Clustering-Based Redshifts
  We demonstrate that observations lacking reliable redshift information, such
as photometric and radio continuum surveys, can produce robust measurements of
cosmological parameters when empowered by clustering-based redshift estimation.
This method infers the redshift distribution based on the spatial clustering of
sources, using cross-correlation with a reference dataset with known redshifts.
Applying this method to the existing SDSS photometric galaxies, and projecting
to future radio continuum surveys, we show that sources can be efficiently
divided into several redshift bins, increasing their ability to constrain
cosmological parameters. We forecast constraints on the dark-energy
equation-of-state and on local non-gaussianity parameters. We explore several
pertinent issues, including the tradeoff between including more sources versus
minimizing the overlap between bins, the shot-noise limitations on binning, and
the predicted performance of the method at high redshifts. Remarkably, we find
that, once this technique is implemented, constraints on dynamical dark energy
from the SDSS imaging catalog can be competitive with, or better than, those
from the spectroscopic BOSS survey and even future planned experiments.
Further, constraints on primordial non-Gaussianity from future large-sky
radio-continuum surveys can outperform those from the Planck CMB experiment,
and rival those from future spectroscopic galaxy surveys. The application of
this method thus holds tremendous promise for cosmology.
