Equivariant Vision Networks for Predicting Planetary Systems' Architectures


The architecture of planetary systems, including the number of planets and their orbital configurations, provides crucial insights into their formation and evolution. This project aims to leverage the capabilities of equivariant computer vision networks to predict the number of planets in observed systems from astronomical data. Equivariant networks, due to their ability to handle rotational and reflectional symmetries inherent in astronomical images, offer a promising approach for analyzing spatial data without loss of predictive accuracy due to orientation changes. By regressing on the number of planets, this project seeks to develop a robust model that can adapt to the complexities of observational data, including direct images, transit data, and radial velocity measurements.


Total project length: 175/350 hours.

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