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A Bayesian non-parametric method to detect clusters in Planck data
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A Bayesian non-parametric method to detect clusters in Planck data
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We show how one may expect a significant number of SZ detections in future Planck data without any of the typical assumptions needed in present component separation methods, such as about the power spectrum or the frequency dependence of any of the components, circular symmetry or a typical scale for the clusters. We reduce the background by subtracting an estimate of the point sources, dust and CMB. The final SZE map is estimated in Fourier space. The catalogue of returned clusters is complete above flux 200 mJy (353 GHz) while the lowest flux reached by our method is about 70 mJy (353 GHz). We predict a large number of detections (about 9000) in 4/5 of the sky. This large number of SZ detections will allow a robust and consistent analysis of the evolution of the cluster population with redshift and will have important implications for determining the best cosmological model.
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