A new approach for processing X-ray data for detecting mini-halos in galaxy clusters

Abstract

Galaxy clusters, which may contain hundreds or thousands of galaxies, are the biggest gravitationally bound objects in the universe. These clusters are filled with heated X-ray emitting plasma. Radio mini-halos are radio sources which are found in some cool-core non-major merging galaxy clusters and study of these mini halos helped scientists to unveil many other mysteries of how the universe works. Till now 23 such galaxy clusters have been found, many with confirmed minihalo and some with potential mini-halos. Even a few years ago observational data of this kind of astronomical observations were available only to the astronomical communities and specific research institutions. As more and more data is publicly available it made a way for Amateur astronomers and students who want to work with real data. In our work we use such publicly accessible X-ray data of these galaxy clusters from Chandra X-ray Observatory for determining the cold front which ultimately will give us the location of these mini halos. The main goal of our thesis is to develop a workflow using some open-source Astro data processing packages and including our own python script for such purpose. These software tools include CIAO, ClusterPyXT pipeline, conda and our own python script. This workflow includes setting up the proper software environments, list of packages needed, rules for choosing different input parameters for downloading as well as processing data successfully and some sources to compare the generated output. From start to end this process can be successfully run on any moderate modern desktop computer.