Physical modelling of galaxy clusters and Bayesian inference in astrophysics

TL;DR

This paper compares galaxy cluster mass estimates from AMI radio data and Planck satellite data using Bayesian models, introduces a new nested sampling algorithm, and explores improvements in physical cluster modeling.

Contribution

It presents a comprehensive Bayesian analysis framework for galaxy clusters, compares different models and data sources, and introduces a novel geometric nested sampling algorithm.

Findings

Bayesian models effectively estimate cluster masses from AMI and Planck data.

The new geometric nested sampler improves sampling efficiency.

Physical model modifications enhance cluster parameter inference.

Abstract

I 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 Navarro-Frenk-White (NFW) and generalised-NFW profiles respectively. I also analyse simulated AMI data with input values based on PwS mass estimates. I then compare three cluster models using AMI data for the 54 cluster sample. The two observational models considered only model the gas content of the cluster. To compare the physical and observational models I consider their posterior parameter estimates, including the calculation of a metric defined between two probability distributions. The models' fit to the cluster data is evaluated by looking at the Bayesian evidence values. Improvements to the physical modelling of galaxy clusters are then considered, either by relaxing some of the assumptions underlying the physical model, or by introducing a new profile for the dark matter component of clusters. The final part of the cluster analysis work focuses on Bayesian analysis using a joint likelihood function of data from both AMI and the Planck satellite simultaneously. Finally, a new Bayesian inference algorithm based on nested sampling is presented. The algorithm, named the "geometric nested sampler", is an adaption of the Metropolis-Hastings nested sampler and makes use of the geometrical interpretation of sets of parameters to sample from their domains efficiently. The geometric nested sampler is tested on several toy models as well as a model representing the emission of gravitational waves from binary black hole mergers.