Photometric Redshift Estimation Using Spectral Connectivity Analysis

Understand

The development of fast and accurate methods of photometric redshift estimation is a vital step towards being able to fully utilize the data of next-generation surveys within precision cosmology.

• In this paper we apply a specific approach to spectral connectivity analysis (SCA; Lee & Wasserman 2009) called diffusion map.
• SCA is a class of non-linear techniques for transforming observed data (e.g., photometric colours for each galaxy, where the data lie on a complex subset of p-dimensional space) to a simpler, more natural coordinate system wherein we apply regression to make redshift predictions.
• As SCA relies upon eigen-decomposition, our training set size is limited to ~ 10,000 galaxies; we use the Nystrom extension to quickly estimate diffusion coordinates for objects not in the training set.