Abstract
The Sunyaev–Zel’dovich effect is the ideal probe for investigating the outskirts of galaxy clusters. To map this signal, we apply a spectral imaging technique which combines parametric component separation and sparse representations. Our procedure is an improved version of an existing algorithm, which now features a better treatment of astrophysical contaminants, and the implementation of a new beam deconvolution. We use the most recent frequency maps delivered byPlanck, and we consider the clusters analysed in theXMMCluster Outskirts Project (X-COP). In particular, we focus on the images of two clusters which may be possibly interacting with neighbouring objects, namely A2029 and RXCJ1825. We also highlight the advantages of the new beam deconvolution method, through a comparison with the original version of the imaging algorithm.
Highlights
Galaxy clusters are the largest virialized structures in the universe
Focussing on the twelve massive and nearby clusters analysed in XMM Cluster Outskirts Project (X-COP), we show that our method is suitable to recover anisotropies located beyond R500 in their tSZ maps, with improved reliability and stability with respect to the original implementation of the algorithm
Following [7], we modelled the temperature signal in each High Frequency Instrument (HFI) map as the sum of the Ns sources to separate, si, multiplied by their spectral energy density, fi
Summary
Galaxy clusters are the largest virialized structures in the universe. As prescribed by the hierarchical scenario, they form by accretion and merging of smaller collapsed objects. An optimal strategy to characterize cluster outskirts through observations is the combination of X-ray data with measurements of the Sunyaev–Zel’dovich (SZ) effect [2]. In this context, the recent XMM Cluster Outskirts Project (X-COP) [3] exploits the synergy between X-ray and tSZ data.
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