Measuring the true distance of objects seen on the celestial sphere is a challenge that has plagued astronomy from its earliest days. The classical solution to this problem for extragalactic systems has been measuring the regression velocity through spectroscopy to determine a redshift-related distance. However in this age of large scale surveys, these measurements are “expensive” and the ratio of known distances to objects observed is rapidly decreasing. While alternative methods have been developed using the expected spectral energy distribution of these sources (i.e. photometric redshifts), they have been fraught with uncertainties and are already approaching their fundamental limits with respect to accuracy and utility.

In this talk, I will present the clustering redshift technique: a redshift inference method utilizing the intrinsic clustering of matter in the universe. I will demonstrate the accuracy and sensitivity of this method with currently available data. Using this technique, we have been able to measure the entire redshift distributions of sources ranging from galaxies in SDSS and 2MASS, to unresolved sources in Planck. I will highlighting errors that creep into the photometric redshift estimates that remain degenerate with colour information alone. The success of this method has opened up vast unexplored realms of astronomy, providing us with a new way of measuring the three-dimensional structure of the universe.