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We use a new approach based on self-supervised deep learning networksoriginally applied to transparency separation in order to simultaneouslyextract the components of the extragalactic submillimeter sky, namely theCosmic Microwave Background (CMB), the Cosmic Infrared Background (CIB), andthe Sunyaev-Zel'dovich (SZ) effect. In this proof-of-concept paper, we test ourapproach on the WebSky extragalactic simulation maps in a range of frequenciesfrom 93 to 545 GHz, and compare with one of the state-of-the-art traditionalmethod MILCA for the case of SZ. We compare first visually the images, and thenstatistically the full-sky reconstructed high-resolution maps with powerspectra. We study the contamination from other components with cross spectra,and we particularly emphasize the correlation between the CIB and the SZ effectand compute SZ fluxes around positions of galaxy clusters. The independentnetworks learn how to reconstruct the different components with lesscontamination than MILCA. Although this is tested here in ideal case (withoutnoise, beams, nor foregrounds), this method shows good potential forapplication in future experiments such as Simons Observatory (SO) incombination with the Planck satellite.

Self-supervised component separation for the extragalactic submillimeter sky