Clusters, Groups, and Filaments in the Chandra Deep Field-South up to Redshift 1

Abstract

We present a comprehensive structure detection analysis of the 0.3 square degree area of the MUSYC-ACES field which covers the Chandra Deep Field-South (CDFS). Using a density-based clustering algorithm on the MUSYC and ACES photometric and spectroscopic catalogues we find 62 over-dense regions up to redshifts of 1, including, clusters, groups and filaments. All structures are confirmed using the DBSCAN method, including the detection of nine structures previously reported in the literature. We present a catalogue of all structures present including their central position, mean redshift, velocity dispersions, and classification based on their morphological and spectroscopic distributions. In particular we find 13 galaxy clusters and 6 large groups/small clusters. Comparison of these massive structures with published XMM-Newton imaging (where available) shows that $80\%$ of these structures are associated with diffuse, soft-band (0.4 - 1 keV) X-ray emission including $90\%$ of all objects classified as clusters. The presence of soft-band X-ray emission in these massive structures (${\mathrm{M}}_{200}\ge 4.9\times 10^{13}{\mathrm{M}}_{\odot }$) provides a strong independent confirmation of our methodology and classification scheme. Nearly $60\%$ of the clusters, groups and filaments are detected in the known enhanced density regions of the CDFS at $z\simeq 0.13$, $z\simeq 0.52$, $0.68$, and $0.73$. Additionally, all of the clusters, bar the most distant, are found in these over-dense redshifts regions. Many of the clusters and groups exhibit signs of on-going formation seen in their velocity distributions, position within the detected cosmic web and in one case through the presence of tidally disrupted central galaxies exhibiting trails of stars. These results all provide strong support for hierarchical structure formation up to redshifts of 1.

Accepted to AJ, 18 pages, 31 figures, 4 tables

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• Data mining techniques on astronomical spectra data. I : Clustering Analysis
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