2024 Project Description

Delving into the intricacies of large-scale structure formation

Supervisors

Rogerio Monteiro-Oliveira
Find out more about supervisors on ASIAA website

Task Description and Goals

Galaxy clusters represent the largest structures in the Universe that are (almost) in equilibrium. Despite their name, they are predominantly composed of dark matter (80%) and a hot, X-ray-emitting intra-cluster gas (15%). Only a small fraction, 5%, of their mass consists of galaxies. These clusters form through the merger of smaller structures in a highly energetic process, comparable even to the Big Bang. Depending on the merger configuration (e.g. bullet cluster), the three components can be temporarily stripped out from the common potential well, providing us with a valuable opportunity to study them in detail. For example, merging galaxy clusters are often referred to as astrophysical particle colliders, helping to investigate the nature of dark matter.

However, several important aspects necessary for a comprehensive description of the merger process are not fully understood yet, such as: (1) Why do some merging clusters not show any detachments among their components? (2) How can we determine with a single observation if a cluster pair is a pre- or post-merger system? (3) Given that the clusters' halos are disrupted immediately after the collision, what is the impact on measuring the corresponding masses via weak gravitational lensing, assuming an analytical mass profile?

To help address these and other questions, we will utilize observational data derived from the remarkable products of the Subaru Survey and/or numerical simulations of merging clusters. Our primary objective is to identify the most noteworthy objects and commence a spectroscopic follow-up to provide: (i) a 3D characterization of the current motion; (ii) a comprehensive description of the merger kinematics; (iii) enhancements to the weak gravitational lensing mass maps. The numerical simulations will bolster our interpretations drawn from observational data.

Required Background

• Passion for science and curiosity.
• Willingness to learn.
• At least basic programming abilities in python.
• At least intermediate English communication and writing.