Deep learning identification of asteroids interacting with g-s secular resonances

Secular resonances occur when there is a commensurability between the fundamental frequencies of asteroids and planets. These interactions can affect orbital elements like eccentricity and inclination. In this work, our focus is to study the g−g6−s+s6 resonance, which affects highly inclined asteroids in the inner main belt around the Phocaea family. Traditionally, the identification of these asteroids was done manually, which demanded a significant amount of time and became unfeasible due to the large volume of data. Our goal is to develop deep learning models for the automatic identification of asteroids affected by this resonance. In this work, Convolutional Neural Network (CNN) models, such as VGG, Inception, and ResNet, as well as the Vision Transformer (ViT) architecture, are used. To evaluate the performance of the models, we used metrics such as accuracy, precision, recall, and F1-score, applied to both filtered and unfiltered elements. We applied deep learning methods and evaluated which one presented the best effectiveness in the classification of asteroids affected by the secular resonance. To improve the performance of the models, we employed regularization techniques, such as data augmentation and dropout. CNN models demonstrated excellent performance with both filtered and unfiltered elements, but the Vision architecture stood out, providing exceptional performance across all used metrics and low processing times.