We developed a Python based framework for astronomical image processing and analysis. Astronomical image loading, normalizing, stacking, and filtering processes represent visible range images from grayscale. Besides, the blending process helps to analyze the image of multiple wavelengths in the visible range. The methods take advantage of include median filtering for noise reduction, unsharp masking for sharpening details, and intensity normalization techniques. The detailed analysis of pixel intensity distributions and applying Gaussian fitting to variations across different wavelength bands. These methods highlight Python as a valuable tool for astronomers.

[NASA / ADS] [GitHub] [arXiv:2410.06573v1]

Supervisor: Dr. Khan Muhammad Bin Asad | Astronomer and Assistant Professor, Department of Physical Sciences, Independent University Bangladesh

A course of study on star formation and life-cycle, which delved into the physics governing the properties of a star. To simulate a sun-like star on Python and plot the variation of its properties. By solving the differential equations for mass, density, pressure, and luminosity variations, along with the temperature gradients for convective and radiative transfer, and the equation of state. The plots describe the variations of the star’s properties and what they mean for the life and death of the star.

GitHub: github.com/mdfardinxyz/Stellar-Evolution-H-R-Diagram-

Supervisor: Dr. Khan Muhammad Bin Asad | Astronomer and Assistant Professor, Department of Physical Sciences, Independent University Bangladesh

In this study, we used a deep learning model to detect the Higgs boson by utilizing the HIGGS dataset, which contains a large amount of particle collision data. Our neural network model, which was trained on 800,000 data, uses backpropagation and layers of neurons to maximize its predictions. The model's accuracy during five training epochs was 47.01%. This outcome shows how deep learning methods can be used to tackle challenging particle physics issues. 

GitHub: github.com/mdfardinxyz/Deep-Learning-Higgs-Boson-Dataset-/tree/main

Advisor: Mir Sazzat Hossain | Junior Research Scientist, Center for Computational & Data Sciences, Independent University Bangladesh. 

This is a compilation of the tasks assigned in my Sophomore Year's Computational Physics course. This course helped me learn Python more realistically than any other programming course I had taken before. 

GitHub: github.com/mdfardinxyz/Computational-Physics

Advisor: Dr. M Arshad Momen | Visiting Scientist, CERN, Theoretical Physicist & Professor, Department of Physical Sciences, Independent University Bangladesh

Consultant: Dr. K Ayaz Rabbani, Environmental Scientist & Associate Professor, Department of Environmental Science, Independent University Bangladesh

Aim is to learn how to construct (numerically) visual representations and analyze the stability and phase transitions in a Skyrme-type model based on Bose-Einstein condensates (BECs), also known as the BEC Skyrme model.

These explorations are critical in deciphering the understanding of soliton dynamics in dense quantum systems like Neutron stars, which may also shed light on the quantum aspects of Black Hole entropy through analogous topological features.

GitHub: github.com/mdfardinxyz/BEC-Skyrme-model

Supervisor: Dr. M Arshad Momen | Visiting Scientist, CERN, Theoretical Physicist & Professor, Department of Physical Sciences, Independent University Bangladesh

The image titled "Exosky" challenges observers to visualize the night sky from one of the approximately 5500 exoplanets discovered by astronomers. The challenge was to simulate exoplanet skies using NASA Exoplanet Archive data and star catalogs. Participants had to develop an app or interface that allows users to draw constellations, visualize them on a computer or virtual reality device, or print them as high-quality images. 

Our team was unable to participate in the global stage due to some deadline concerns. But it did not prevent us from slipping behind. This project was so appealing and unique that we kept working on it and are still on it.  

GitHub: github.com/mdfardinxyz/exosky

Team: IUB Astra [Reference]

TART is a low-cost open-source array radio telescope consisting of 24 all-sky receivers operating at 1.575 GHz. It observes the entire sky continuously and is optimized to detect transient events, including satellites, near-earth objects, and high-energy cosmic rays. 

Small Transient Array Radio Telescope (START) is under construction at Center for Astronomy, Space Science and Astrophysics (CASSA), Independent University, Bangladesh (IUB) 

This will be a go-to radio telescope for only teaching purposes that anyone can build. A proper documentation is still under process and yet to be published. This is a collaborative initiative with a group of TART from New Zealand.

Supervisor: Dr. Khan Muhammad Bin Asad | Astronomer and Director of Center for Astronomy, Space Science and Astrophysics (CASSA)

TART Official Site: tart.elec.ac.nz/

START: cassa.site/research/projects/