Deep learning for Sunyaev-Zel'dovich detection in Planck
  The Planck collaboration has extensively used the six Planck HFI frequency
maps to detect the Sunyaev-Zel'dovich (SZ) effect with dedicated methods, e.g.,
by applying (i) component separation to construct a full sky map of the y
parameter or (ii) matched multi-filters to detect galaxy clusters via their hot
gas. Although powerful, these methods may still introduce biases in the
detection of the sources or in the reconstruction of the SZ signal due to prior
knowledge (e.g., the use of the GNFW profile model as a proxy for the shape of
galaxy clusters, which is accurate on average but not on individual clusters).
In this study, we use deep learning algorithms, more specifically a U-Net
architecture network, to detect the SZ signal from the Planck HFI frequency
maps. The U-Net shows very good performance, recovering the Planck clusters in
a test area. In the full sky, Planck clusters are also recovered, together with
more than 18,000 other potential SZ sources, for which we have statistical
hints of galaxy cluster signatures by stacking at their positions several full
sky maps at different wavelengths (i.e., the CMB lensing map from Planck, maps
of galaxy over-densities, and the ROSAT X-ray map). The diffuse SZ emission is
also recovered around known large-scale structures such as Shapley, A399-A401,
Coma, and Leo. Results shown in this proof-of-concept study are promising for
potential future detection of galaxy clusters with low SZ pressure with this
kind of approach, and more generally for potential identification and
characterisation of large-scale structures of the Universe via their hot gas.
