Physical modelling of galaxy clusters detected by the Planck satellite. Issue 3 (16th November 2018)

• Record Type: Journal Article
• Authors: Javid, Kamran; Olamaie, Malak; Perrott, Yvette C; Carvalho, Pedro; Grainge, Keith J B; Hobson, Michael P; Rumsey, Clare; Saunders, Richard D E
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 483:Issue 3(2019)
• Page Start: 3529
• Page End: 3544
• Publication Date: 2018-11-16
• Subjects: methods: data analysis -- galaxies: clusters: general -- cosmology: observations; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/sty3115
• Languages: English
• ISSNs: 0035-8711

Abstract

ABSTRACT: We present a comparison of mass estimates for 54 galaxy cluster candidates from the second Planck catalogue (PSZ2) of Sunyaev–Zel'dovich sources. We compare the mass values obtained with data taken from the Arcminute Microkelvin Imager (AMI) radio interferometer system and from the Planck satellite. The former of these uses a Bayesian analysis pipeline that parametrizes a cluster in terms of its physical quantities, and models the dark matter and baryonic components of a cluster using Navarro–Frenk–White (NFW) and generalized-NFW profiles, respectively. Our mass estimates derived from Planck data are obtained from the results of the Bayesian detection algorithm PowellSnakes, are based on the methodology detailed in the PSZ2 paper, and produce two sets of mass estimates; one estimate is calculated directly from the angular radius θ – integrated Comptonization parameter Y posterior distributions, and the other uses a 'slicing function' to provide information on θ based on X-ray measurements and previous Planck mission samples. We find that for 37 of the clusters, the AMI mass estimates are lower than both values obtained from Planck data. However the AMI and slicing function estimates are within one combined standard deviation of each other for 31 clusters. We also generate cluster simulations based on the slicing-function mass estimates, and analyse them in the same way as we did the real AMI data. We find that inclusion in the simulations of radio-source confusion, CMB noise and measurable radio-sources causes AMI mass estimates to be systematically low.