Celestial Machine Learning: Discovering the Planarity, Heliocentricity, and Orbital Equation of Mars with AI Feynman
This work addresses the challenge of automating scientific discovery in astronomy, though it is incremental as it extends an existing method to a specific historical case.
The researchers tackled the problem of discovering Kepler's First Law for Mars' elliptical orbit from astronomical data using AI Feynman, achieving symbolic regression that emulated Kepler's discoveries of heliocentricity and planarity.
Can a machine or algorithm discover or learn the elliptical orbit of Mars from astronomical sightings alone? Johannes Kepler required two paradigm shifts to discover his First Law regarding the elliptical orbit of Mars. Firstly, a shift from the geocentric to the heliocentric frame of reference. Secondly, the reduction of the orbit of Mars from a three- to a two-dimensional space. We extend AI Feynman, a physics-inspired tool for symbolic regression, to discover the heliocentricity and planarity of Mars' orbit and emulate his discovery of Kepler's first law.