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Artificial neural networks (ANN) have been successfully used in the lastyears to identify patterns in astronomical images. The use of ANN in the fieldof asteroid dynamics has been, however, so far somewhat limited. In this workwe used for the first time ANN for the purpose of automatically identifying thebehaviour of asteroid orbits affected by the M1:2 mean-motion resonance withMars. Our model was able to perform well above 85% levels for identifyingimages of asteroid resonant arguments in term of standard metrics likeaccuracy, precision and recall, allowing to identify the orbital type of allnumbered asteroids in the region. Using supervised machine learning methods,optimized through the use of genetic algorithms, we also predicted the orbitalstatus of all multi-opposition asteroids in the area. We confirm that the M1:2resonance mainly affects the orbits of the Massalia, Nysa, and Vesta asteroidfamilies.

Artificial Neural Network classification of asteroids in the M1:2 mean-motion resonance with Mars