Physical modelling of galaxy clusters detected by the Planck satellite
Grainge, Keith JB
Monthly Notices of the Royal Astronomical Society
Oxford University Press
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Javid, K., Olamaie, M., Perrott, Y. C., Carvalho, P., Grainge, K. J., Hobson, M., Rumsey, C., & et al. (2019). Physical modelling of galaxy clusters detected by the Planck satellite. Monthly Notices of the Royal Astronomical Society, 483 (3), 3529-3544. https://doi.org/10.1093/mnras/sty3115
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 parameterises a cluster in terms of its physical quantities, and models the dark matter & baryonic components of a cluster using NFW and GNFW profiles respectively. Our mass estimates derived from Planck data are obtained from the results of the Bayesian detection algorithm PowellSnakes (PwS), 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 $\theta$ - integrated Comptonisation parameter $Y$ posterior distributions, and the other uses a `slicing function' to provide information on $\theta$ 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.
This work was performed using the Darwin Supercomputer of the University of Cambridge High Performance Computing (HPC) Service (http://www.hpc.cam.ac.uk/), provided by Dell Inc. using Strategic Research Infrastructure Funding from the Higher Education Funding Council for England and funding from the Science and Technology Facilities Council ... Kamran Javid acknowledges an STFC studentship. Yvette Perrott acknowledges support from a Trinity College Junior Research Fellowship.
External DOI: https://doi.org/10.1093/mnras/sty3115
This record's URL: https://www.repository.cam.ac.uk/handle/1810/291493
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