Artificial Neural Network classification of asteroids in the M1:2 mean-motion resonance with Mars
This work addresses the limited use of ANN in asteroid dynamics, providing an incremental improvement for astronomers by automating orbital classification in a specific resonance.
The researchers tackled the problem of automatically identifying asteroid orbits affected by the M1:2 mean-motion resonance with Mars using artificial neural networks, achieving over 85% accuracy in classification and predicting orbital status for all multi-opposition asteroids in the region.
Artificial neural networks (ANN) have been successfully used in the last years to identify patterns in astronomical images. The use of ANN in the field of asteroid dynamics has been, however, so far somewhat limited. In this work we used for the first time ANN for the purpose of automatically identifying the behaviour of asteroid orbits affected by the M1:2 mean-motion resonance with Mars. Our model was able to perform well above 85% levels for identifying images of asteroid resonant arguments in term of standard metrics like accuracy, precision and recall, allowing to identify the orbital type of all numbered asteroids in the region. Using supervised machine learning methods, optimized through the use of genetic algorithms, we also predicted the orbital status of all multi-opposition asteroids in the area. We confirm that the M1:2 resonance mainly affects the orbits of the Massalia, Nysa, and Vesta asteroid families.