probing interacting dark energy (IDE) with galaxy density fields using LoTSS DR2 data

Project Description:

The accelerated expansion of the universe remains one of the most profound mysteries in modern cosmology. While the standard LCDM model assumes non-interacting dark matter and dark energy, interacting dark energy (IDE) models propose that these components exchange energy through a coupling, altering the expansion history and growth rate of structure. Low-redshift galaxy surveys provide a unique opportunity to test these models by analyzing the clustering properties of galaxies, which are sensitive to modifications in the growth of structure. This project involves reducing and analyzing galaxy density fields from the LoTSS DR2 (LOFAR Two-Meter Sky Survey Data Release 2) catalog to probe IDE models. For this project, the student will measure the galaxy auto-correlation function, which quantifies the clustering of galaxies, and compare it with predictions from $\LambdaDM and IDE scenarios. By constraining interaction parameters such as the coupling strength (ξ), we aim to address three key questions: Do the observed clustering patterns favor deviations from $\Lambda$CDM? What is the scale dependence of the growth rate of structure under IDE models? How do radio galaxies trace large-scale structure compared to optical surveys? This study will contribute to the broader effort to understand the nature of dark energy and its potential interactions with dark matter, while also providing insights into the astrophysical properties of radio galaxies.

Research Area:

Astronomy

Project Level:

Honours

This Project Is Offered At The Following Node(s):

(UCT)(UKZN)(NWU)

Special Requirements:

For this Honours project, students will be expected to analyze LoTSS DR2 catalogs to construct galaxy overdensity maps and compute the galaxy auto-power spectrum , which characterizes the clustering of galaxies in Fourier space. Alternatively, the two-point correlation function (ξ(r)) can be computed in real space and transformed into the power spectrum using Fourier techniques. They will need to be familiar with Linux and have some Python programming experience. They should be willing to learn how to use cosmological analysis tools such as Namater, Corrfunc, HEALPix, or CCL (Cosmology Library). Prior experience with statistical methods (e.g., Fisher matrix or likelihood analysis) is an added advantage but not required.