Reducing Photometric Redshift Uncertainties Using Galaxy Clustering Information

Abstract

In cosmology and astronomy, measuring the distances to galaxies is an important task. This is done by measuring the redshift of the spectra from these sources. Photometric redshifts are a quick way of estimating the true redshifts and are calculated using color band intensities. The errors on these measurements produce an apparent anisotropy in the radial direction. One of our fundamental assumptions in cosmology is that the universe is isotropic. The ultimate goal of this project is to recover isotropy in the photometric redshift data. This is done using Bayesian statistical inference to update the probability distribution functions for the galaxy redshifts. We also demonstrate a simplified method of estimating the underlying density field. Ultimately, we found that this simplified method failed to generate significant improvements in photometric redshift uncertainties. Work on this project is ongoing. When completed, we expect to find that we have produced a robust system capable of complementing photometric redshift estimators and improving their results. With these improved measurements it is possible to better constrain various cosmological parameters important to dark-matter research.