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

TL;DR

This paper demonstrates the successful application of artificial neural networks to automatically classify asteroid orbits affected by the M1:2 resonance with Mars, achieving high accuracy and predicting orbital statuses.

Contribution

First application of ANN to identify asteroid orbital behavior in the M1:2 resonance with Mars, using genetic algorithms for optimization.

Findings

ANN achieved over 85% accuracy in classifying resonant asteroid images

The model effectively identified orbital types of all numbered asteroids in the region

Resonance mainly affects Massalia, Nysa, and Vesta asteroid families.

Abstract

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.