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gnaneshwar46/README.md

Gnaneshwar G S

Physics graduate with research experience in computational galaxy evolution, ML-driven structural inference, and scaling relation analysis using large astronomical survey datasets.

Research Focus

  • Galaxy mass–size relations
  • Morphology-dependent scaling behavior
  • Structural drivers of galaxy evolution
  • SMBH–host galaxy correlations
  • Observational survey data analysis

Selected Projects

Galaxy Structure Inference from Mass–Size Geometry

Hypothesis-driven structural inference of galaxy morphology using NASA–Sloan Atlas data (z < 0.08). Geometric boundary analysis reveals a mass–size slope ≈ 2 with systematic redshift evolution.

🔗 https://github.com/gnaneshwar46/galaxy-structure-inference

SMBH–Host Galaxy Scaling Relations

Observational analysis of SMBH proxy behaviour across morphologically distinct galaxy populations. Results show scaling relations emerge primarily in bulge-dominated systems.

🔗 https://github.com/gnaneshwar46/SMBH-host-galaxy-scaling

Technical Stack

  • Python (NumPy, Pandas, Matplotlib, SciPy, Astropy)
  • Survey data handling (SDSS / NSA)
  • Cosmology-based physical unit conversions
  • Regression modeling and scaling-relation analysis
  • Reproducible research repository design

Research Approach

I aim to connect observational data to physical interpretation through structured, transparent, and reproducible scientific workflows.

Pinned Loading

  1. galaxy-structure-inference galaxy-structure-inference Public

    Hypothesis-driven structural inference of galaxy morphology from mass–size geometry in the local universe (z < 0.08)

    Python

  2. SMBH-host-galaxy-scaling SMBH-host-galaxy-scaling Public

    Observational analysis of AGN host galaxy scaling relations using SDSS and NASA-Sloan Atlas data.

    Python