Research Projects

This project aims to address critical issues related to galaxy formation and evolution by focusing on two research areas: dark matter distribution in galaxies and gas and star formation. The Lambda-CDM model of cosmology has several shortcomings in small galactic scales, such as the 'core-cusp' problem, the 'too big to fail' problem, and the'missing-satellite' problem. These problems violate predicted dark matter distributions in and around galaxies. In relation to gas and star formation, two significant issues persist: how galaxies acquire sufficient gas to sustain prolonged star formation, and how star formation physics leads to global scaling relations at much larger scales. To address these issues, extensive observations of nearby galaxies and sophisticated numerical modeling are planned. The atomic Hydrogen (HI) serves as an excellent tracer of physical activities and dynamics in galaxies, making deep HI observations of a large number of galaxies imperative. To analyze radio interferometric data, a software pipeline is developed to analyze radio interferometric data without manual intervention. Machine learning and artificial intelligence (ML/AI)-based algorithms are implemented to efficiently identify data corrupted by low-level RFI. This pipeline can be optimized for future generation large telescopes like the Square Kilometer Array (SKA), which cannot be analyzed manually due to their large volume of data and storage limitations. The IIT Indore Radio Interferometer (IIRI) is also used to identify and filter out RFI, one of the key bottlenecks at the IIRI site.