The sensitivity of GPz estimates of photo-z posterior PDFs to
  realistically complex training set imperfections
  The accurate estimation of photometric redshifts is crucial to many upcoming
galaxy surveys, for example the Vera C. Rubin Observatory Legacy Survey of
Space and Time (LSST). Almost all Rubin extragalactic and cosmological science
requires accurate and precise calculation of photometric redshifts; many
diverse approaches to this problem are currently in the process of being
developed, validated, and tested. In this work, we use the photometric redshift
code GPz to examine two realistically complex training set imperfections
scenarios for machine learning based photometric redshift calculation: i) where
the spectroscopic training set has a very different distribution in
colour-magnitude space to the test set, and ii) where the effect of emission
line confusion causes a fraction of the training spectroscopic sample to not
have the true redshift. By evaluating the sensitivity of GPz to a range of
increasingly severe imperfections, with a range of metrics (both of photo-z
point estimates as well as posterior probability distribution functions, PDFs),
we quantify the degree to which predictions get worse with higher degrees of
degradation. In particular we find that there is a substantial drop-off in
photo-z quality when line-confusion goes above ~1%, and sample incompleteness
below a redshift of 1.5, for an experimental setup using data from the Buzzard
Flock synthetic sky catalogues.
