CPz: Classification-aided photometric-redshift estimation
  Broadband photometry offers a time and cost effective method to reconstruct
the continuum emission of celestial objects. Thus, photometric redshift
estimation has supported the scientific exploitation of extragalactic
multiwavelength surveys for more than twenty years. In the era of precision
cosmology, with the upcoming Euclid and LSST surveys, very tight constraints
are put on the expected performance of photometric redshift estimation using
broadband photometry, thus new methods have to be developed in order to reach
the required performance. We present a novel automatic method of optimizing
photometric redshift performance, the classification-aided photometric redshift
estimation (CPz). The main feature of CPz is the unified treatment of all
classes of objects detected in extragalactic surveys: galaxies of any type
(passive, starforming and starbursts), active galactic nuclei (AGN),
quasi-stellar objects (QSO), stars and also includes the identification of
potential photometric redshift catastrophic outliers. The method operates in
three stages. First, the photometric catalog is confronted with star, galaxy
and QSO model templates by means of spectral energy distribution fitting.
Second, three machine-learning classifiers are used to identify 1) the
probability of each source to be a star, 2) the optimal photometric redshift
model library set-up for each source and 3) the probability to be a photometric
redshift catastrophic outlier. Lastly, the final sample is assembled by
identifying the probability thresholds to be applied on the outcome of each of
the three classifiers. We apply our method to the near-infrared VISTA public
surveys, matched with optical photometry from CFHTLS, KIDS and SDSS,
mid-infrared WISE photometry. We show that CPz offers improved photometric
redshift performance for both normal galaxies and AGN without the need for
extra X-ray information. [abridged]
