Clustering-based redshift estimation: method and application to data
  We present a data-driven method to infer the redshift distribution of an
arbitrary dataset based on spatial cross-correlation with a reference
population and we apply it to various datasets across the electromagnetic
spectrum to show its potential and limitations. Our approach advocates the use
of clustering measurements on all available scales, in contrast to previous
works focusing only on linear scales. We also show how its accuracy can be
enhanced by optimally sampling a dataset within its photometric space rather
than applying the estimator globally. We show that the ultimate goal of this
technique is to characterize the mapping between the space of photometric
observables and redshift space as this characterization then allows us to infer
the clustering-redshift p.d.f. of a single galaxy. We apply this technique to
estimate the redshift distributions of luminous red galaxies and emission line
galaxies from the SDSS, infrared sources from WISE and radio sources from
FIRST. We show that consistent redshift distributions are found using both
quasars and absorber systems as reference populations. This technique brings
valuable information on the third dimension of astronomical datasets. It is
widely applicable to a large range of extra-galactic surveys.
