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An extended catalog of Sunyaev–Zeldovich objects from Planck data with deep learning

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Gilfanov,  M. R.
High Energy Astrophysics, MPI for Astrophysics, Max Planck Society;

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Sunyaev,  R. A.
High Energy Astrophysics, MPI for Astrophysics, Max Planck Society;

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Citation

Meshcheryakov, A. V., Nemeshaeva, A., Burenin, R. A., Gilfanov, M. R., & Sunyaev, R. A. (2022). An extended catalog of Sunyaev–Zeldovich objects from Planck data with deep learning. Astronomy Letters-a Journal of Astronomy and Space Astrophysics, 48(9), 479-496. doi:10.1134/S1063773722090055.


Cite as: https://hdl.handle.net/21.11116/0000-000D-173F-4
Abstract
We present an extended catalog of Sunyaev–Zeldovich (SZ) sources from Planck data based on a combination of the samples obtained by two independent approaches to the detection of objects in microwave data: the segmentation of sources by a deep learning (U-net) model on the intensity maps in the 100–850 GHz HFI channels is supplemented by the search for SZ sources on the prepared (NILC) Compton y-parameter maps. By comparing our sample of Planck SZ objects with the most complete catalogs of galaxy clusters obtained from microwave (PSZ2, ACT) and X-ray (the preliminary 1-year SRG/eROSITA catalog of clusters in the eastern Galactic sky; MCXC from ROSAT observations) sky surveys, we have analyzed the galaxy cluster mass (M500), redshift (z), and X-ray flux (FX,0.5−2) selection functions. We see two scenarios for using our extended catalogs of SZ sources from Planck data: (i) the sample of only reliable SZ objects from the catalog of candidates can be used directly, for example, to search for unique objects in it or to investigate the population of clusters and (ii) the entire catalog of SZ objects contains a considerable number of spurious sources, but, at the same time, has almost all of the possible detected galaxy clusters in the Planck data (it can be used in combination with other catalogs of candidates for galaxy clusters obtained independently from data in other ranges or with other instruments).