My research focuses on the intersection of observation, computation, and theory. My current and ongoing research projects are:
Testing 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.
Supervisors: Dr. Tanveer Karim; Dr. Georgios Valogiannis
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.
Supervisors: Dr. AKM Mahbubur Rahman; Dr. Amin Ahsan Ali; Dr. Arshad Momen (PI)
GitHub RepoPhysics-Informed Neural Networks for Cosmological Inflation (Exploratory Work)
Explores the application of Physics-Informed Neural Networks (PINNs) to model stochastic inflation in early universe cosmology. This exploratory work investigates how machine learning techniques can be integrated with physical constraints to solve differential equations governing inflationary dynamics.
Supervisor: Dr. Rafid Mahbub
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.
Supervisors: Dr. Soebur Razzaque; Dr. Saeeda Sajjad
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.
Supervisors: Dr. Khan Muhammad Bin Asad, Dr. AKM Mahbubur Rahman; Dr. Amin Ahsan Ali, Dr. Arshad Momen (PI)
ExploreRadio Frequency Interference Identification and Elimination
This research is on identifying and eliminating radio frequency interference in astronomical observations. It addresses one of the most critical challenges in radio astronomy, where interference from human-made sources can significantly impact the quality of astronomical data.
Supervisors: Dr. Khan Muhammad Bin Asad, Dr. AKM Mahbubur Rahman, Dr. Arshad Momen (PI)
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