  We use a new approach based on self-supervised deep learning networks
originally applied to transparency separation in order to simultaneously
extract the components of the extragalactic submillimeter sky, namely the
cosmic microwave background (CMB), the cosmic infrared background (CIB), and
the Sunyaev-Zel'dovich (SZ) effect. In this proof-of-concept paper, we test our
approach on the WebSky extragalactic simulation maps in a range of frequencies
from 93 to 545 GHz, and compare with one of the state-of-the-art traditional
methods, MILCA, for the case of SZ. We first visually compare the images, and
then statistically analyse the full-sky reconstructed high-resolution maps with
power spectra. We study the contamination from other components with cross
spectra, and particularly emphasise the correlation between the CIB and the SZ
effect and compute SZ fluxes around positions of galaxy clusters. The
independent networks learn how to reconstruct the different components with
less contamination than MILCA. Although this is tested here in an ideal case
(without noise, beams, or foregrounds), this method shows significant potential
for application in future experiments such as the Simons Observatory (SO) in
combination with the Planck satellite.
