The Good, the Bad and the Ugly: Statistical quality assessment of SZ
  detections
  We examine three approaches to the problem of source classification in
catalogues. Our goal is to determine the confidence with which the elements in
these catalogues can be distinguished in populations on the basis of their
spectral energy distribution (SED). Our analysis is based on the projection of
the measurements onto a comprehensive SED model of the main signals in the
considered range of frequencies. We first first consider likelihood analysis,
which half way between supervised and unsupervised methods. Next, we
investigate an unsupervised clustering technique. Finally, we consider a
supervised classifier based on Artificial Neural Networks. We illustrate the
approach and results using catalogues from various surveys. i.e., X-Rays
(MCXC), optical (SDSS) and millimetric (Planck Sunyaev-Zeldovich (SZ)). We show
that the results from the statistical classifications of the three methods are
in very good agreement with each others, although the supervised neural
network-based classification shows better performances allowing the best
separation into populations of reliable and unreliable sources in catalogues.
The latest method was applied to the SZ sources detected by the Planck
satellite. It led to a classification assessing and thereby agreeing with the
reliability assessment published in the Planck SZ catalogue. Our method could
easily be applied to catalogues from future large survey such as SRG/eROSITA
and Euclid.
