The photometric redshift of a given object corresponds to the best fit
of its photometric SED by the set of template spectra, in general through
a standard
minimization procedure. The observed SED of a given galaxy is compared
to a set of template spectra:
A combination of this method with the Bayesian marginalization introducing
an a priori probability was proposed by Benítez (2000): he
demonstrated that in this case the dispersion of
can be significantly improved. Despite of this result, we decided to not
introduce such type of information, because the application of the Bayesian
technique can introduce spurious effects in particular studies. However,
this method can be regarded with interest when the goal is some specific
application or when one is dealing with poor data, in such a way that the
introduction of hints allows to obtain useful results. Alternatively, the
photometric redshift estimate can be safely improved introducing the Bayesian
inference when prior information is not related to the photometric properties
of sources. Examples of such priors that could be combined with the
technique are the morphology or the clues inferred from gravitational lensing
modeling. In these cases, the user can easily introduce the interesting
prior in the Fortran 77 code.
The major advantages of the SED fitting technique are its simplicity
and the fact that it does not require any spectroscopic sample. Its weak
point is mainly related to the need to choose fiducial spectral templates
valid for all objects. We will discuss our choice in the following.