Particle Astrophysics Seminar: Hidden Signals in a Population of Galaxy Clusters

  • Aug. 10, 2020, 2:00 pm US/Central
  • Arya Farahi, University of Michigan

Abstract: Galaxy-clusters (clusters) are the most massive gravitationally collapsed structures in the Universe. It is established that, on average, the observables of clusters scale with their mass. This scaling relation has been successfully capitalized to constrain the cosmological parameters. Each cluster was formed at a different time and experienced a different formation history. Additionally, their baryonic properties are altered due to stochastic energy and momentum feedback processes. These processes produce detectable correlated scatter about the halo mass—observable relation. However, detecting these correlated scatters are currently limited by averaging and stacking methods. Though intuitive and easy to implement, these approaches suffer from a significant loss of information. During this talk, I will present our work on retrieving and exploiting this previously unexplored information.

First, I will introduce a population-based framework that enables us to unpack and build upon the information contained in the correlated observables. As a case study, I will illustrate the impact of the formation time on observable properties of clusters as well as the process to extract this information from observational data. By employing our population-based framework, I will demonstrate that while the existing observational data agree with simulation-based predictions, there are predictions that have not been confirmed yet. Finally, I will identify rising methodological opportunities and challenges to achieve the unmet need in detecting these correlated signals from growing observational data. I will present my recent work on developing a set of novel data analysis tools that allows population studies with large multi-wavelength data collected by deep and wide surveys, such as Dark Energy Survey, SPT, and eROSITA.

For more information, please contact Yu-Dai Tsai at