The effect of emission lines on the performance of photometric redshift
  estimation algorithms
  We investigate the effect of strong emission line galaxies on the performance
of empirical photometric redshift estimation methods. In order to artificially
control the contribution of photometric error and emission lines to total flux,
we develop a PCA-based stochastic mock catalogue generation technique that
allows for generating infinite signal-to-noise ratio model spectra with
realistic emission lines on top of theoretical stellar continua. Instead of
running the computationally expensive stellar population synthesis and nebular
emission codes, our algorithm generates realistic spectra with a statistical
approach, and - as an alternative to attempting to constrain the priors on
input model parameters - works by matching output observational parameters.
Hence, it can be used to match the luminosity, colour, emission line and
photometric error distribution of any photometric sample with sufficient
flux-calibrated spectroscopic follow-up. We test three simple empirical
photometric estimation methods and compare the results with and without
photometric noise and strong emission lines. While photometric noise clearly
dominates the uncertainty of photometric redshift estimates, the key findings
are that emission lines play a significant role in resolving colour space
degeneracies and good spectroscopic coverage of the entire colour space is
necessary to achieve good results with empirical photo-z methods. Template
fitting methods, on the other hand, must use a template set with sufficient
variation in emission line strengths and ratios, or even better, first estimate
the redshift empirically and fit the colours with templates at the best-fit
redshift to calculate the K-correction and various physical parameters.
