Physical modelling of galaxy clusters detected by the<i>Planck</i>satellite

Javid, Kamran; Olamaie, Malak; Perrott, Y. C.; Carvalho, P.; Grainge, Keith; Hobson, M.; Rumsey, C.; Saunders, Richard D. E.

doi:10.1093/mnras/sty3115

Cited by 7 publications

(1 citation statement)

References 57 publications

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“…This paper provides a follow-up to the work presented in Javid et al (2019) (from here on referred to as KJ19), in which we performed Bayesian inference on data obtained with the Arcminute Microkelvin Imager (AMI) radio interferometer system, to derive estimates of physical properties of clusters that have been detected by Planck. In this paper we focus on the observational properties of clusters obtained from telescopes such as AMI and Planck which measure the Sunyaev-Zel'dovich (SZ, Sunyaev and Zeldovich 1970) effect: the angular radius, θ, and the integrated Comptonisation parameter, Y .…”

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confidence: 99%

Comparison of physical and observational galaxy cluster modelling

Javid,

Perrott,

Hobson

et al. 2018

Preprint

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We present a comparison between three cluster models applied to data obtained by the Arcminute Microkelvin Imager radio interferometer system. The physical model (PM) parameterises a cluster in terms of its physical quantities to model the dark matter and baryonic components of the cluster using Navarro-Frenk-White (NFW) and generalised-NFW profiles respectively. The observational models (OM I and OM II) model only the gas content of the cluster. The two OMs vary only in the priors they use in Bayesian inference: OM I has a joint prior on angular radius θ and integrated Comptonisation Y, derived from simulations, while OM II uses separable priors on θ and Y which are based on calculations of the physical model. For the comparison we consider a sample of 54 clusters which are a subsample of the second Planck catalogue of Sunyaev-Zel'dovich sources. We first compare the Y estimates of the three models, and find that the PM generally yields lower estimates relative to the OMs. We then compute the Earth Mover's Distance between the θ -Y posterior distributions obtained from each model for each cluster, and find that the two models which are most discrepant are PM and OM I. Finally, we compare the Bayesian evidence values obtained from each model for each cluster. OM I generally provides the best fit to the data but not at a statistically significant level, according to the Jeffreys scale. The highest evidence ratio obtained is actually in favour of the PM over OM I.

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Section: Introductionmentioning

confidence: 99%

Comparison of physical and observational galaxy cluster modelling

Javid,

Perrott,

Hobson

et al. 2018

Preprint

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Physical modelling of galaxy cluster Sunyaev–Zel’dovich data using Einasto dark matter profiles

Javid

Perrott

Rumsey

et al. 2019

Monthly Notices of the Royal Astronomical Society

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We derive a model for Sunyaev-Zel'dovich data from a galaxy cluster which uses an Einasto profile to model the cluster's dark matter component. This model is similar to the physical models for clusters previously used by the Arcminute Microkelvin Imager (AMI) consortium, which model the dark matter using a Navarro-Frenk-White (NFW) profile, but the Einasto profile provides an extra degree of freedom. We thus present a comparison between two physical models which differ only in the way they model dark matter: one which uses an NFW profile (PM I) and one that uses an Einasto profile (PM II). We illustrate the differences between the models by plotting physical properties of clusters as a function of cluster radius. We generate AMI simulations of clusters which are created and analysed with both models. From this we find that for 14 of the 16 simulations, the Bayesian evidence gives no preference to either of the models according to the Jeffreys scale, and for the other two simulations, weak preference in favour of the correct model. However, for the mass estimates obtained from the analyses, the values were within 1σ of the input values for 14 out of 16 of the clusters when using the correct model, but only in 6 out of 16 cases when the incorrect model was used to analyse the data. Finally we apply the models to real data from cluster A611 obtained with AMI, and find the mass estimates to be consistent with one another except in the case of when PM II is applied using an extreme value for the Einasto shape parameter.

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Sunyaev–Zel’dovich profile fitting with joint AMI-Planck analysis

Perrott

Javid

Carvalho³

et al. 2019

Monthly Notices of the Royal Astronomical Society

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We develop a Bayesian method of analysing Sunyaev-Zel'dovich measurements of galaxy clusters obtained from the Arcminute Microkelvin Imager (AMI) radio interferometer system and from the Planck satellite, using a joint likelihood function for the data from both instruments. Our method is applicable to any combination of Planck data with interferometric data from one or more arrays. We apply the analysis to simulated clusters and find that when the cluster pressure profile is known a-priori, the joint dataset provides precise and accurate constraints on the cluster parameters, removing the need for external information to reduce the parameter degeneracy. When the pressure profile deviates from that assumed for the fit, the constraints become biased. Allowing the pressure profile shape parameters to vary in the analysis allows an unbiased recovery of the integrated cluster signal and produces constraints on some shape parameters, depending on the angular size of the cluster. When applied to real data from Planck-detected cluster PSZ2 G063.80+11.42, our method resolves the discrepancy between the AMI and Planck Y -estimates and usefully constrains the gas pressure profile shape parameters at intermediate and large radii.

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Physical modelling of galaxy clusters detected by thePlancksatellite

Cited by 7 publications

References 57 publications

Comparison of physical and observational galaxy cluster modelling

Comparison of physical and observational galaxy cluster modelling

Physical modelling of galaxy cluster Sunyaev–Zel’dovich data using Einasto dark matter profiles

Sunyaev–Zel’dovich profile fitting with joint AMI-Planck analysis

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