The search for worlds beyond our solar system has yielded over 5,000 confirmed exoplanets, but raw astronomical data tells only part of their story. Transit photometry, radial velocity measurements, and atmospheric spectroscopy generate massive datasets that require sophisticated analysis to reveal planetary characteristics like orbital dynamics, atmospheric composition, and potential habitability. The Exoplanet Analysis Suite tackles this computational challenge head-on, providing researchers with a comprehensive toolkit to extract meaningful insights from complex observational data.
Built around multiple machine learning models implemented in Jupyter notebooks, this suite processes diverse exoplanet datasets through classification algorithms, regression models, and statistical analysis pipelines. The application integrates transit detection algorithms, stellar characterization tools, and planetary parameter estimation methods into a cohesive analytical framework. What sets it apart is the interactive web interface that transforms notebook-based analysis into an accessible platform where researchers can visualize light curves, explore parameter correlations, and generate publication-ready plots without diving into code.
This tool bridges the gap between raw astronomical observations and scientific discovery, making exoplanet analysis accessible to both seasoned researchers and students entering the field. Whether you’re characterizing super-Earths in the habitable zone or studying hot Jupiter atmospheres, the suite’s modular architecture adapts to diverse research questions while maintaining the rigor demanded by modern exoplanet science.
⭐ Stars: 14
💻 Language: Jupyter Notebook
🔗 Repository: KumawatCodes/Exoplanet-Analysis-Suite