A Bayesian Hierarchical Model for estimating the heterogeneity of pressure profiles within a population of galaxy clusters

The study of the thermodynamic properties of galaxy clusters is crucial to probing the evolution of the universe at the largest scales. In this talk, I will present a Bayesian Hierarchical Model tailored to determining the average pressure profile and the corresponding intrinsic scatter for a population of galaxy clusters. Such analysis adequately encapsulates the global properties of the overall distribution of clusters and each cluster’s specific, individual aspects. Taking into account the heterogeneity of the pressure profile across different galaxy clusters within a population is not straightforward and, especially, is highly computationally expensive, since it requires a combined estimate of the objects all at the same time, in a single-stage procedure. I will give an overview of the advantages and challenges of such methodology, with a particular focus on its great potential of exploiting the massive amounts of observations gathered by the current and forthcoming surveys that characterize the Big Data era in astrophysics.

Brief CV of Dr. Fabio Castagna:

He is currently a postdoc researcher at INAF-Osservatorio Astronomico di Brera, focused on characterizing the thermodynamic properties of galaxy clusters through SZ and X-ray observations. After graduating in Biostatistics from Bocconi University in Milan in 2018, he began collaborating with INAF-Brera on the applications of Bayesian statistics to astrophysics. In 2024, he obtained his PhD in Computer Science and Mathematics of Computation from the University of Insubria.