Complementarily to X-ray observations, the thermal SZ effect is a powerful tool to probe the baryonic content of galaxy clusters from their core to their peripheries. While contaminations by astrophysical and instrumental backgrounds require us to scan the thermal SZ signal across various frequencies, the multi-scale nature of cluster morphologies require us to observe such objects at various angular resolutions. We developed component separation algorithms that take advantage of sparse representations to combine these heterogeneous pieces of information, separate the thermal SZ signal from its contaminants, detect and map the thermal SZ signal of galaxy clusters from nearby to more distant clusters of the Planck catalogue. Spatially weighted likelihoods allow us in particular to connect parametric fittings of the component Spectral Energy Distribution with wavelet and curvelet imaging, but also to combine signals registered with beams of various width. Such techniques already allow us to detect sub-structures in the peripheries of nearby clusters with Planck, and could be extended to observations performed at higher angular resolutions.
Extracting the thermal SZ signal from heterogeneous millimeter data sets