Volume 580, August 2015
|Number of page(s)||17|
|Section||Numerical methods and codes|
|Published online||19 August 2015|
The Good, the Bad, and the Ugly: Statistical quality assessment of SZ detections
Institut d’Astrophysique Spatiale, CNRS (UMR 8617) Université Paris-Sud
11, Bâtiment 121,
2 Instituto de Física de Cantabria (CSIC-Universidad de Cantabria), Avda. de los Castros s/n, 39005 Santander, Spain
3 Laboratoire de Physique Subatomique et de Cosmologie, Université Grenoble-Alpes, CNRS/IN2P3, Institut National Polytechnique de Grenoble, 53 rue des Martyrs, 38026 Grenoble Cedex, France
4 CNRS, IRAP, 9 Av. colonel Roche, BP 44346, 31028 Toulouse Cedex 4, France
5 Laboratoire AIM, IRFU/Service d’Astrophysique – CEA/DSM – CNRS – Université Paris Diderot, Bât. 709, CEA-Saclay, 91191 Gif-sur-Yvette Cedex, France
Accepted: 13 February 2015
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 consider likelihood analysis, which is halfway 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, such as 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 other, although the supervised neural network-based classification shows better performance 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 surveys such as SRG/eROSITA and Euclid.
Key words: methods: statistical / galaxies: clusters: general
© ESO, 2015
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