Deep learning for Sunyaev-Zel'dovich detection in Planck

Abstract

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.