Galaxy Bulge Regions with SDSS

I completed a semester-long applied data analysis project through the Emerging Scholars Program at CUNY New York City College of Technology, focused on extracting statistical relationships from large astronomical datasets. This project marked my transition into Python-based data analysis and established the technical foundation for my subsequent research work.

Using a subset of approximately one million galaxies from the Sloan Digital Sky Survey, I analyzed correlations between galaxy bulge and disk properties and global characteristics such as stellar mass and redshift. I implemented data cleaning, binning, and aggregation workflows to reduce noise and reveal population-level trends, and produced visualizations to support interpretation of the results.

The analysis identified strong linear relationships between structural size and total stellar mass, as well as a non-linear dependence of bulge mass fraction on galaxy mass. These results demonstrate how large observational datasets can be used to constrain models of galaxy formation and evolution.

I presented this work as a poster at a campus-wide undergraduate research symposium, and the project served as the technical on-ramp for later work involving large survey datasets, uncertainty-aware analysis, and scalable Python workflows. The code for the project is in this GitHub repository.