Abstract
We present the first combination of a thermal Sunyaev-Zel’dovich (tSZ) map with a multi-frequency quality assessment of the sky pixels based on artificial neural networks with the aim being to detect tSZ sources from submillimeter observations of the sky by Planck. We present the construction of the resulting filtered and cleaned tSZ map, MILCANN. We show that this combination leads to a significant reduction of noise fluctuations and foreground residuals compared to standard reconstructions of tSZ maps. From the MILCANN map, we constructed a tSZ source catalog of about 4000 sources with a purity of 90%. Finally, we compare this catalog with ancillary catalogs and show that the galaxy-cluster candidates in our catalog are essentially low-mass (down to M500 = 1014 M⊙) high-redshift (up to z ≤ 1) galaxy cluster candidates.
Highlights
Introduction frequency ν isAs the largest virialized structures in the Universe, galaxy clusters are excellent tracers of matter distribution
We present the first combination of a thermal Sunyaev-Zel’dovich map with a multi-frequency quality assessment of the sky pixels based on artificial neural networks with the aim being to detect tSZ sources from submillimeter observations of the sky by Planck
We have shown qualitatively that the MILCANN tSZ map presents a significantly reduced background compared to the input MILCA tSZ map
Summary
As the largest virialized structures in the Universe, galaxy clusters are excellent tracers of matter distribution. A method based on artificial neural networks (ANN) was proposed by Aghanim et al (2015) This uses the Planck multi-frequency data to assess the quality of the tSZ sources by decomposing the measured signal into the different astrophysical components contributing to the Planck frequencies. This new quality assessment method was applied to validate the Planck cluster catalog (Planck Collaboration XXVII 2016). For the present analysis we used several publicly available datasets (catalogs and surveys) either to describe astrophysical source properties or to characterize the galaxy cluster candidates detected in the ANN-weighted y maps. We use catalogs of clusters detected in X-rays (MCXC, Piffaretti et al 2011, and reference therein) and in the SDSS survey, namely WHL12 (Wen et al.2012), WHL15 (Wen & Han 2015), WHY18 (Wen et al 2018), and redMaPPer (Rykoff et al 2014)
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