Simulating Orbital Data for Exoplanet Detection

This project explores the orbital dynamics of celestial bodies through an N-body simulation, with the goal of generating realistic synthetic orbital data. The long-term aim is to develop machine learning techniques for exoplanet detection based on these simulations.

By simulating the motion of stars, planets, and other objects under gravitational interaction, this project creates data that mimics what might be observed by telescopes. This synthetic data can then be used to train models for recognizing subtle patterns—like wobbles or light curves—that indicate the presence of an exoplanet.

The simulation is written in Python, leveraging numerical methods and physical modeling to approximate planetary systems.

🔗 View the code on GitHub

Stay tuned—more visualizations, animations, and machine learning experiments coming soon!