My research focuses on the intersection of observation, computation, and theory. My current and ongoing research projects are:
Radio Frequency Interference Identification and Elimination
A sophisticated system designed to identify and eliminate radio frequency interference in astronomical observations. This research addresses one of the most critical challenges in radio astronomy, where interference from human-made sources can significantly impact the quality of astronomical data.
Read more GitHub RepoRadha Gobinda Chandra, a Radio Galaxy Classifier
An advanced machine learning-based classifier designed to identify and categorize radio galaxies from astronomical survey data. This tool leverages deep learning techniques to automatically classify different types of radio sources, significantly improving the efficiency of radio astronomy research.
ExploreTesting Modified Gravity theories via galaxy clustering and CMB lensing data
This project forecasts signatures of modified gravity (including f(R) and DGP gravity) using cross-correlation analyses between galaxy clustering and CMB lensing. Incorporates simulation and survey specifications (LSST/DESC, Simons Observatory), Fisher matrix forecasting, and fast emulators for theoretical predictions.
GitHub RepoMachine Learning for High-Energy Physics Jet Classification
The Jet-Tagging-Experiments repository develops an explainable Particle Chebyshev Network that uses physics-motivated interaction features (ln Δ, ln kT, ln z, ln m²) to classify jets on CERN Open Data (JetClass), advancing QCD-informed machine learning for particle physics.
GitHub RepoMachine Learning for GRB Data & Redshift Estimation
Contains tools and experiments focused on using machine learning to estimate redshifts and classify gamma-ray bursts using Fermi and other astrophysical data. Explores advanced algorithms and astrophysical feature engineering for transient phenomena.
GitHub Repo