MILCANN: A tSZ map for galaxy cluster detection assessed using a neural network
2021
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 M 500 = 1014 M ⊙ ) high-redshift (up to z ≤ 1) galaxy cluster candidates.
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