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  • 1
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2022
    In:  Monthly Notices of the Royal Astronomical Society Vol. 514, No. 4 ( 2022-07-06), p. 4696-4717
    In: Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP), Vol. 514, No. 4 ( 2022-07-06), p. 4696-4717
    Abstract: We measure the velocity dispersions of clusters of galaxies selected by the red-sequence Matched-filter Probabilistic Percolation (redMaPPer) algorithm in the first three years of data from the Dark Energy Survey (DES), allowing us to probe cluster selection and richness estimation, λ, in light of cluster dynamics. Our sample consists of 126 clusters with sufficient spectroscopy for individual velocity dispersion estimates. We examine the correlations between cluster velocity dispersion, richness, X-ray temperature, and luminosity, as well as central galaxy velocity offsets. The velocity dispersion–richness relation exhibits a bimodal distribution. The majority of clusters follow scaling relations between velocity dispersion, richness, and X-ray properties similar to those found for previous samples; however, there is a significant population of clusters with velocity dispersions that are high for their richness. These clusters account for roughly 22 per cent of the λ & lt; 70 systems in our sample, but more than half (55 per cent) of λ & lt; 70 clusters at z & gt; 0.5. A couple of these systems are hot and X-ray bright as expected for massive clusters with richnesses that appear to have been underestimated, but most appear to have high velocity dispersions for their X-ray properties likely due to line-of-sight structure. These results suggest that projection effects contribute significantly to redMaPPer selection, particularly at higher redshifts and lower richnesses. The redMaPPer determined richnesses for the velocity dispersion outliers are consistent with their X-ray properties, but several are X-ray undetected and deeper data are needed to understand their nature.
    Type of Medium: Online Resource
    ISSN: 0035-8711 , 1365-2966
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2016084-7
    SSG: 16,12
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