Creating spectral templates from multicolor redshift surveys

Understanding how the physical properties of galaxies (e.g., their spectral type or age) evolve as a function of redshift relies on having an accurate representation of galaxy spectral energy distributions. While it has been known for some time that galaxy spectra can be reconstructed from a handful of orthogonal basis templates, the underlying basis is poorly constrained. The limiting factor has been the lack of large samples of galaxies (coveri ng a wide range in spectral type) with high signal-to-noise spectrophotomet ric observations. To alleviate this problem we introduce here a new techniq ue for reconstructing galaxy spectral energy distributions directly from sa mples of galaxies with broadband photometric data and spectroscopic redshif ts. Exploiting the statistical approach of the Karhunen-Loeve expansion, ou r iterative training procedure increasingly improves the eigenbasis, so tha t it provides better agreement with the photometry. We demonstrate the util ity of this approach by applying these improved spectral energy distributio ns to the estimation of photometric redshifts for the HDF sample of galaxie s. We find that in a small number of iterations the dispersion in the photo metric redshifts estimator (a comparison between predicted and measured red shifts) can decrease by up to a factor of 2.