A core finding of the study was the systematic increase in the Mass-to-Luminosity ratio as a function of radius. While the inner regions of most galaxies showed a close fit to baryonic mass predictions, the outer regions required an "extended halo" of dark matter to remain stable. This research quantified the "turn-over point" where dark matter begins to dominate the gravitational potential of the galaxy.
Dark Matter Distribution and Galactic Mass Profiling
Pradyota Phaneesh
OVERVIEW
Conducted a comprehensive computational study to map dark matter distributions across various morphological galaxy types (Spiral, Elliptical, Lenticular, and Irregular). By analyzing rotational velocities and photometric data from the SPARC (Spitzer Photometry and Accurate Rotation Curves) database, this research aimed to quantify the discrepancy between visible baryonic mass and total gravitational mass.
The project involved deriving mass profiles to demonstrate that visible matter alone cannot account for observed orbital velocities, providing empirical evidence for the "dark matter halo" hypothesis across different galactic structures
HighlightS
Gravitational Mass Derivation: Mathematically derived enclosed mass profiles by applying Newtonian gravitation (F=r2GMm) and centripetal acceleration (ac=rv2) to observed rotation curves.
M/L Ratio Analysis: Calculated Mass-to-Luminosity (M/L) ratios through the numerical integration of photometric surface brightness, revealing the dominance of non-luminous matter in outer galactic regions.
Multi-Morphology Study: Analyzed cross-categorical data to identify how dark matter influence varies between structured spiral galaxies and chaotic irregular galaxies.
Automated Data Pipeline: Developed custom Python scripts to automate data extraction from the SPARC database, streamlining the processing of hundreds of galactic data points.
Peer-Reviewed Publication: Co-authored and published findings in the Spring Research Journal (2024), contributing to the academic discourse on galactic dark matter halos.
SKILLS
AstrophysicsComputational PhysicsProgramming & Data ScienceScientific Communication
ADDITIONAL CONTENTS
Pradyota Phaneesh
Mechanical Engineering & Advanced Manufacturing
Mechanical engineer with expertise in advanced manufacturing, CAD design, and finite element analysis. Experienced in optimizing production processes, designing complex mechanical systems, and conducting experimental research in sustainable nanomanufacturing. Proficient in ANSYS, MATLAB, and industry-standard CAD tools. Demonstrated ability to reduce manufacturing defects, improve first-pass yield, and lead cross-functional engineering teams.
Mechanical engineer with expertise in advanced manufacturing, CAD design, and finite element analysis. Experienced in optimizing production processes, designing complex mechanical systems, and conducting experimental research in sustainable nanomanufacturing. Proficient in ANSYS, MATLAB, and industry-standard CAD tools. Demonstrated ability to reduce manufacturing defects, improve first-pass yield, and lead cross-functional engineering teams.