Papers › The XMM Cluster Survey: Automating the estimation of hydrostatic mass for large...
The XMM Cluster Survey: Automating the estimation of hydrostatic mass for large samples of galaxy clusters I -- Methodology, Validation, & Application to the SDSSRM-XCS sample
D. J. Turner, P. A. Giles, A. K. Romer, J. Pilling, T. K. Lingard, R. Wilkinson, M. Hilton, E. W. Upsdell, R. Al-Serkal, T. Cheng, R. Eappen, P. J. Rooney, S. Bhargava, C. A. Collins, J. Mayers, C. Miller, R. C. Nichol, M. Sahén, P. T. P. Viana
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We describe features of the X-ray: Generate and Analyse (XGA) open-source software package that have been developed to facilitate automated hydrostatic mass (M_(hydro)) measurements from XMM X-ray observations of clusters of galaxies. This includes describing how XGA measures global, and radial, X-ray properties of galaxy clusters. We then demonstrate the reliability of XGA by comparing simple X-ray properties, namely the X-ray temperature and gas mass, with published values presented by the XMM Cluster Survey (XCS), the Ultimate XMM eXtragaLactic survey project (XXL), and the Local Cluster Substructure Survey (LoCuSS). XGA measured values for temperature are, on average, within 1% of the values reported in the literature for each sample. XGA gas masses for XXL clusters are shown to be ∼10% lower than previous measurements (though the difference is only significant at the ∼1.8σ level), LoCuSS R₂₅₀₀ and R₅₀₀ gas mass re-measurements are 3% and 7% lower respectively (representing a 1.5σ and 3.5σ difference). Like-for-like comparisons of hydrostatic mass are made to LoCuSS results, which show that our measurements are 10±3% (19±7%) higher for R₂₅₀₀ (R₅₀₀). The comparison between R₅₀₀ masses shows significant scatter. Finally, we present new M_(hydro) measurements for 104 clusters from the SDSS DR8 redMaPPer XCS sample (SDSSRM-XCS). Our SDSSRM-XCS hydrostatic mass measurements are in good agreement with multiple literature estimates, and represent one of the largest samples of consistently measured hydrostatic masses. We have demonstrated that XGA is a powerful tool for X-ray analysis of clusters; it will render complex-to-measure X-ray properties accessible to non-specialists.
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