Degradation analysis in the estimation of photometric redshifts from
  non-representative training sets
  We perform an analysis of photometric redshifts estimated by using a
non-representative training sets in magnitude space. We use the ANNz2 and GPz
algorithms to estimate the photometric redshift both in simulations as well as
in real data from the Sloan Digital Sky Survey (DR12). We show that for the
representative case, the results obtained by using both algorithms have the
same quality, either using magnitudes or colours as input. In order to reduce
the errors when estimating the redshifts with a non-representative training
set, we perform the training in colour space. We estimate the quality of our
results by using a mock catalogue which is split samples cuts in the $r$-band
between $19.4< r< 20.8$. We obtain slightly better results with GPz on single
point z-phot estimates in the complete training set case, however the
photometric redshifts estimated with ANNz2 algorithm allows us to obtain mildly
better results in deeper $r$-band cuts when estimating the full redshift
distribution of the sample in the incomplete training set case. By using a
cumulative distribution function and a Monte-Carlo process, we manage to define
a photometric estimator which fits well the spectroscopic distribution of
galaxies in the mock testing set, but with a larger scatter. To complete this
work, we perform an analysis of the impact on the detection of clusters via
density of galaxies in a field by using the photometric redshifts obtained with
a non-representative training set.
