A comprehensive study investigating Type Ia supernovae spectral analysis using customized Spextractor. This project aims to query the supernova spectral data and determines the velocity, peW and more features by a single code. This project will make the supernova cosmology study more robust and ensure the proper data availability.

An analytical pipeline buildup to identify and characterize cold fronts in galaxy clusters with semi-automation. Leveraging CIAO and DS9, this research processes high-resolution X-ray imagery to get the map and for exclusive Curvelet transformation's implementation. Additionally, this project focuses on finding the radio minihalos in those galaxy clusters.

Developing an accurate and fully automated Point Spread Function (PSF) model to correct optical distortions and detect the potential Intracluster Light candidates. We are using different Python libraries including SExtractor and Photutils to detect the faint object with more efficiency. Our first works are ongoing with DESI Legacy Survey.

This project explores the use of Physics-Informed Neural Networks (PINNs) to model stochastic inflation in early-universe cosmology. It investigates how machine learning techniques can be combined with physical constraints to solve the differential equations governing inflationary dynamics.