Predicting Asteroid Physical Properties from Photometric Data Using Machine Learning

Accurately determining the physical parameters of asteroids, such as shape, rotation period, and rotation axis orientation, is crucial to understanding their structure and evolution, as well as to planning space missions. Since direct observation of the shapes of these objects is usually impossible, these parameters are determined indirectly through photometric observations. This paper describes a method to support this process using machine learning techniques. The final model developed - a two-branch convolutional neural network combining light-curve analysis with geometric metadata - achieves a coefficient of determination (R²) of about 0.40 on an independent test set, a result significantly above the random baseline.