Dictionary Learning for Photometric Redshift Estimation

Photometric redshift estimation and the assessment of the distance to an astronomic object plays a key role in modern cosmology. We present in this article a new method for photometric redshift estimation that relies on sparse linear representations. The proposed algorithm is based on a sparse decomposition for rest-frame spectra in a learned dictionary. Additionally, it provides both an estimate for the redshift together with the full resolution spectra from the observed photometry for a given galaxy. This technique has been evaluated on realistic simulated photometric measurements.