Free-form modelling of galaxy clusters: a Bayesian and data-driven approach

Understand

A new method is presented for modelling the physical properties of galaxy clusters.

• Our technique moves away from the traditional approach of assuming specific parameterised functional forms for the variation of physical quantities within the cluster, and instead allows for a 'free-form' reconstruction, but one for which the level of complexity is determined automatically by the observational data and may depend on position within the cluster.
• This is achieved by representing each independent cluster property as some interpolating or approximating function that is specified by a set of control points, or 'nodes', for which the number of nodes, together with their positions and amplitudes, are allowed to vary and are inferred in a Bayesian manner from the data.
• We illustrate our nodal approach in the case of a spherical cluster by modelling the electron pressure profile Pe(r) in analyses both of simulated Sunyaev-Zel'dovich (SZ) data from the Arcminute MicroKelvin Imager (AMI) and of real AMI observations of the cluster MACS J0744+3927 in the CLASH sample.