Astroinformatics of galaxies and quasars: a new general method for
  photometric redshifts estimation
  With the availability of the huge amounts of data produced by current and
future large multi-band photometric surveys, photometric redshifts have become
a crucial tool for extragalactic astronomy and cosmology. In this paper we
present a novel method, called Weak Gated Experts (WGE), which allows to derive
photometric redshifts through a combination of data mining techniques.
\noindent The WGE, like many other machine learning techniques, is based on the
exploitation of a spectroscopic knowledge base composed by sources for which a
spectroscopic value of the redshift is available. This method achieves a
variance \sigma^2(\Delta z)=2.3x10^{-4} (\sigma^2(\Delta z) =0.08), where
\Delta z = z_{phot} - z_{spec}) for the reconstruction of the photometric
redshifts for the optical galaxies from the SDSS and for the optical quasars
respectively, while the Root Mean Square (RMS) of the \Delta z variable
distributions for the two experiments is respectively equal to 0.021 and 0.35.
The WGE provides also a mechanism for the estimation of the accuracy of each
photometric redshift. We also present and discuss the catalogs obtained for the
optical SDSS galaxies, for the optical candidate quasars extracted from the DR7
SDSS photometric dataset {The sample of SDSS sources on which the accuracy of
the reconstruction has been assessed is composed of bright sources, for a
subset of which spectroscopic redshifts have been measured.}, and for optical
SDSS candidate quasars observed by GALEX in the UV range. The WGE method
exploits the new technological paradigm provided by the Virtual Observatory and
the emerging field of Astroinformatics.
