Research

My research encompasses several different themes, but in a nutshell, it focuses on the analysis of dark energy models and the CMB temperature and polarization signal. It involves the analysis of CMB data using different statistical techniques and extracting cosmological information to study the dynamics of our Universe. My research aims to connect cosmological observations with theory by using different and novel statistical techniques. Below you can find a brief summary of my research.

The cosmological models exhibiting tracker properties have great significance in the context of dark energy as they can reach the present value of dark energy density from a wide range of initial conditions, thereby alleviating both the fine-tuning and the cosmic coincidence problem. The α-attractors, which are originally discussed in the context of inflation, can exhibit the properties of dark energy as they can behave like cosmological trackers at early times and show the late time behaviour of a cosmological constant. In ( MNRAS 511(2022) ), we investigated the Oscillatory Tracker Model (OTM), which belongs to the family of α-attractor dark energy models. Using current observational data sets like CMB, BAO and Type 1A Supernova data, we constrained the parameters of the OTM model. We also studied the effect of the OTM on the CMB temperature and polarization power spectra, matter power spectrum, growth rate, and fσ8. In another work ( arXiv:2209.07167 ), we extended our perturbative studies to Interacting Dark Energy (IDE) models where the interaction between dark energy and dark matter can give rise to comparable energy densities at the present epoch, thereby alleviating the cosmic coincidence problem. Here, along with forecast analysis, we investigated the impact of both the coupling constant and the equation of state parameter of interacting dark energy models on the CMB temperature power spectrum, matter power spectrum, and fσ8.

Recent CMB observations have resulted in very precise observational data. A robust and reliable CMB reconstruction technique can lead to efficient estimation of the cosmological parameters. In ( arXiv:2209.07179 ), we developed an analysis pipeline starting from the reliable estimation of CMB signal and its angular power spectrum to the case of cosmological parameter estimation using the foreground model independent Gibbs-ILC method. We demonstrated the performance of our methodology using simulated temperature and polarization (both E & B) observations using cosmic variance limited future generation PRISM satellite mission. We generated the samples from the joint distribution of cleaned CMB map and its angular power spectrum by implementing the CMB inverse covariance weighted internal-linear-combination (ILC) with the Gibbs sampling technique. After obtaining the reconstructed cleaned CMB angular power spectrum, we estimated the parameters and their corresponding error limits using the MCMC sampling method. The likelihood function estimated by making use of the Blackwell-Rao estimator is used for the estimation of the cosmological parameters. This methodology can estimate tensor-to-scalar ratio r ≥ 0.0075.

The next-generation CMB satellite missions are expected to provide robust constraints on a wide range of cosmological parameters with unprecedented precision. But these constraints on the parameters could weaken if we do not attribute dark energy to a cosmological constant. In ( arXiv:2209.07167 ), we performed a forecast analysis to test the ability of the future generation high-sensitive Cosmic Microwave Background (CMB), and Baryon Acoustic Oscillation (BAO) experiments to constrain phenomenological interacting dark energy models. We consider cosmic variance limited future CMB experiment PICO along with BAO information from the DESI experiment to constrain the parameters of the interacting dark sector. For the forecast analysis, we used simulated temperature and polarization data from PICO within the multipole ranges (ℓ = 2 − 4000). Based on the stability of the cosmological perturbations, we consider two possibilities for the interaction scenario. With the integration of PICO and DESI missions, we observe a significant improvement in the constraints on several cosmological parameters.

The inflationary epoch and the late time acceleration of the expansion rate of universe can be explained by assuming a gravitationally coupled scalar field. In ( J.Astrophys.Astron. 42 (2021) ), we proposed a new method of finding exact solutions in the background of flat Friedmann-Robertson-Walker (FRW) cosmological models by considering both scalar field and matter where the scalar field potential is a function of the scale factor. Our method provides analytical expressions for equation of state parameter of scalar field, deceleration parameter and Hubble parameter. This method can be applied to various other forms of scalar field potential, to the early radiation dominated epoch and very early scalar field dominated inflationary dynamics. Since the method produces exact analytical expression for H(a) (i.e., H(z) as well), we then constrained the model with currents data sets, which includes-Baryon Acoustic Oscillations, Hubble parameter data and Type 1a Supernova data (Pantheon Dataset). As an extension of the method, we also consider the inverse problem of reconstructing scalar field potential energy by assuming any general analytical expression of scalar field equation of state parameter as a function of scale factor.

With the emergence of precision cosmology, discrepancies among key cosmological parameters of the model have also emerged. The inconsistency between the locally measured expansion rate of the universe H₀ and the one inferred from the CMB observations in the context of the standard ΛCDM model has become a significant problem in modern cosmology. Currently, I am working on phenomenological interacting dark energy models where an interaction between dark energy and dark matter can significantly increase the H₀ value. To investigate the H₀ tension, we confront the interacting dark energy models with the latest observational probes like CMB, BAO, Type 1A Supernovae data and Hubble parameter data.-(manuscript under preperation)

This is an ongoing research where I am exploring the application of machine learning in several cosmological scenarios such as CMB data analysis and cosmological parameter estimation. Currently, I am working on the development of a methodology for constraining of various dark energy models using Artificial Neural Networks (ANN) as an alternative to the MCMC method in parameter estimation. To train the ANN model, we use simulated CMB temperature and polarization data, Type 1A Supernovae data, BAO data and the Hubble parameter data.