Learning Spectral Templates for Photometric Redshift Estimation from
  Broadband Photometry
  Estimating redshifts from broadband photometry is often limited by how
accurately we can map the colors of galaxies to an underlying spectral
template. Current techniques utilize spectrophotometric samples of galaxies or
spectra derived from spectral synthesis models. Both of these approaches have
their limitations, either the sample sizes are small and often not
representative of the diversity of galaxy colors or the model colors can be
biased (often as a function of wavelength) which introduces systematics in the
derived redshifts. In this paper we learn the underlying spectral energy
distributions from an ensemble of $\sim$100K galaxies with measured redshifts
and colors. We show that we are able to reconstruct emission and absorption
lines at a significantly higher resolution than the broadband filters used to
measure the photometry for a sample of 20 spectral templates. We find that our
training algorithm reduces the fraction of outliers in the derived photometric
redshifts by up to 28%, bias up to 91%, and scatter up to 25%, when compared to
estimates using a standard set of spectral templates. We discuss the current
limitations of this approach and its applicability for recovering the
underlying properties of galaxies. Our derived templates and the code used to
produce these results are publicly available in a dedicated Github repository:
https://github.com/dirac-institute/photoz_template_learning.
