Comparison of physical and observational galaxy cluster modelling
Kamran Javid, Yvette C. Perrott, Michael P. Hobson, Malak Olamaie,, Clare Rumsey, and Richard D. E. Saunders

TL;DR
This study compares physical and observational models of galaxy clusters using data from the Arcminute Microkelvin Imager, highlighting differences in parameter estimates, model discrepancies, and Bayesian evidence to evaluate model performance.
Contribution
It introduces a comprehensive comparison of three galaxy cluster models, including a physical model and two observational models with different priors, applied to real observational data.
Findings
Physical model yields lower Y estimates than observational models.
The physical and one observational model show the greatest discrepancy in posterior distributions.
Bayesian evidence slightly favors the physical model over the observational models.
Abstract
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 NFW and GNFW 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 and integrated Comptonisation , derived from simulations, while OM II uses separable priors on and which are based on calculations of the physical model. For the comparison we consider a sample of clusters which are a subsample of the second Planck catalogue of Sunyaev-Zel'dovich sources. We first compare the estimates of the three…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
