Members from CAM-SUST come together and collaborate to gain research skills. Every year from the Research and Project sector, projects are launched to teach members on how to carry out a research, how to manage references and how to write a paper with proper styles. The projects are published here in pdf format.
Project 1: Mass of the Central Black Hole of Active Galactic Nuclei: Reverberation Mapping
Authors: Ahmad Al- Imtiaz, K.M Shariat Ullah, Nusrath Jahan, Rahul Dev Sharma, Sakibul Islam Rayhan
Information about Active Galactic Nuclei (AGN’s) mass can help to know more about the host galaxy’s evolution. Using reverberation mapping, we can determine the ‘primary’ mass of the AGN. Here primary mass refers to the mass determined by motion of the nearby gas.
In this study we retrieved spectral data of five Active Galactic Nuclei (AGN) to get these data. We fit two 2D Gaussian curves for a narrow line and a broad line region of the AGN spectra to determine the mass.
Project 2: Correlation Study of Distances Calculated Using Absorption Line Identification and Cosmological Parameters from Plank Data Release
Authors: K M Shariat Ullah, Md. Iftakhar Hossain Simanto, Nafis Shyan and Bayozid Hossen
The vast distance between objects in the universe can be directly calculated by observing its spectra. The red-shift of the object due to the Hubble flow helps to find the relativistic distance of the object. The cosmological parameters can be used to calculate the co-moving distance, the angular distance and the luminosity distance.
The focus of this report was to take spectra of four galaxies and calculate the relativistic distance by identifying the location of the Hα and Hβ lines. Other distances were calculated with cosmological parameters.
Project 3: Image Processing and Analysis of Multiple Wavelength Astronomical Data Using Python Tools
Authors: Tanmoy Bhowmik, MD Fardin Islam, Kazi Nusrat Tasneem, Rantideb Roy, Rownok Shahriar
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.
Project 4: Exploring Evolutionary Track of Individual Stars with Aperture Photometry
Authors: Nusrath Jahan, Abantika Maytra Oishee, and Dibbendu Barua
This project aims to understand different stages of star evolution by constructing a model Hertzsprung-Russell Diagram. In this study, twelve target stars were particularly selected and their data were collected from the publicly available database of SDSS and we used aperture photometry for our analysis.
Our results showed that most of these stars are in the main sequence phase; therefore, they are very important in the star life cycle. We also considered the effectiveness of simple aperture photometry for this kind of analysis. Since our project had to be as simple and accessible as possible, we chose methods that are easy to understand and deliberately avoided complex analyses
