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 distribut
ion (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.
In the standard hot cosmological model, the black-body temperature of the Cosmic Microwave Background (CMB), $T_{rm CMB}$, increases linearly with redshift. Across the line of sight CMB photons interact with the hot ($sim10^{7-8}$ K) and diffuse gas
of electrons from galaxy clusters. This interaction leads to the well known thermal Sunyaev-Zeldovich effect (tSZ), which produces a distortion of the black-body emission law, depending on $T_{rm CMB}$. Using tSZ data from the ${it Planck}$ satellite it is possible to constrain $T_{rm CMB}$ below z=1. Focusing on the redshift dependance of $T_{rm CMB}$ we obtain $T_{rm CMB}(z)=(2.726pm0.001)times (1+z)^{1-beta}$ K with $beta=0.009pm0.017$, improving previous constraints. Combined with measurements of molecular species absorptions, we derive $beta=0.006pm0.013$. These constraints are consistent with the standard (i.e. adiabatic, $beta=0$) Big-Bang model.
