exoproximo

exoproximo

Machine Learning Meets Astronomy

This repository contains machine learning and AI exercises focused on space observation data, particularly exoplanet classification using NASA’s comprehensive archives. It demonstrates the application of modern ML techniques to astronomical discovery and analysis.

The Exoplanet Challenge

NASA’s exoplanet archive contains observations of thousands of confirmed and candidate exoplanets. The challenge is to identify patterns that distinguish confirmed exoplanets from false positives, and to classify exoplanets by their characteristics and potential for resource extraction.

What’s Inside

A collection of Jupyter notebooks exploring:

• Classification Models: Training algorithms to distinguish confirmed exoplanets from stellar noise
• Feature Engineering: Extracting meaningful signals from light curve and transit data
• Data Analysis: Statistical exploration of exoplanet characteristics
• Visualization: Making complex astronomical data interpretable

Data Sources

The project leverages:

• NASA Exoplanet Archive: Kepler, TESS, and other mission data
• Near-Earth Object Database: Asteroid and comet observations
• Stellar Catalogs: Host star characteristics

Machine Learning Applications

Exoplanet data presents unique ML challenges:

Imbalanced Classes: Far more false positives than actual exoplanets Noisy Signals: Distinguishing planetary transits from stellar variability High-Dimensional Data: Complex time-series and spectral data Physical Constraints: Models must respect astrophysical laws

Why This Matters

The rate of exoplanet discovery far exceeds human capacity for manual analysis. Machine learning enables:

• Automated candidate screening
• Pattern recognition across massive datasets
• Discovery of subtle signals humans might miss
• Scalable analysis as new missions collect more data

Technical Skills Demonstrated

• Time-series analysis
• Classification algorithms
• Feature engineering for scientific data
• Working with NASA API and data formats
• Jupyter-based scientific workflows

This project sits at the exciting intersection of data science and astronomy—using computational techniques to advance our understanding of worlds beyond our solar system.