Research Projects

In the last few decades, there has been a paradigm shift in the research in Cosmology. During the time it has transformed from mostly a theory-first subject to an intensely data-driven and multidisciplinary subject. The primary reason is the availability of a plethora of data with unprecedented accuracy from a number of astrophysical and cosmological missions running around the world and in space. To name a few, we have in our hands huge data from Planck 2018 CMB, BOSS-BAO, SnIa, Hubble Space Telescope data etc. Even though almost all the datasets more or less converge to a concordance cosmological model, namely, ΛCDM, there is a number of strong to moderate tensions among different datasets that are haunting cosmologists in recent times. To name a few, there are Hubble tension, growth tension, BAO tension, curvature tension, tension with Quasar data, anomalously low baryon temperature etc as well as recent observations of massive galaxies at high redshifts which can not explained under standard ΛCDM concordance cosmology. So, either a reanalysis of these datasets with new statistical techniques and new interpretations that might possibly resolve some of the tensions, or a reaffirmation of generic existence of these tensions in the datasets that calls for new physics beyond concordance ΛCDM model, is the need of the hour. This has opened up ample scope of looking for New Physics in large cosmological scales as well as statistical applications and data analysis, in particular model independent data analysis with new challenges. On top of that, there are a number of new experimental missions (Euclid, CMB-S4, SKA, Gaia, eLISA etc.) that are expected to be operational in the next couple of years with improved accuracy of measurements. So, forecasting on those missions would presumably be useful to shed some light on how they are going to address those cosmological tensions. In this project, we would like to do a thorough, methodical study using present astrophysical and cosmological data in both model independent and model dependent ways to look for new physics beyond the ΛCDM model that can possibly resolve at least some of the tensions among different data sets. We would also like to use supervised Machine Learning algorithms for re-interpretation and re-examination of the said tensions, thereby addressing the problems from a different perspective. Further, we would forecast on the upcoming missions and search for their possible role in addressing those tensions.