Asteroids co-orbital motion classification based on Machine Learning
Giulia Ciacci Andrea Barucci Sara Di Ruzza Elisa Maria Alessi
In this work, we explore how to classify asteroids in co-orbital motion with
a given planet using Machine Learning. We consider four different kinds of
motion in mean motion resonance with the planet, nominally Tadpole, Horseshoe
and Quasi-satellite, building 3 datasets defined as Real (taking the
ephemerides of real asteroids from the JPL Horizons system), Ideal and
Perturbed (both simulated, obtained by propagating initial conditions
considering two different dynamical systems) for training and testing the
Machine Learning algorithms in different conditions.
  The time series of the variable theta (angle related to the resonance) are
studied with a data analysis pipeline defined ad hoc for the problem and
composed by: data creation and annotation, time series features extraction
thanks to the tsfresh package (potentially followed by selection and
standardization) and the application of Machine Learning algorithms for
Dimensionality Reduction and Classification. Such approach, based on features
extracted from the time series, allows to work with a smaller number of data
with respect to Deep Learning algorithms, also allowing to define a ranking of
the importance of the features. Physical Interpretability of the features is
another key point of this approach. In addition, we introduce the SHapley
Additive exPlanations for Explainability technique.
  Different training and test sets are used, in order to understand the power
and the limits of our approach. The results show how the algorithms are able to
identify and classify correctly the time series, with a high degree of
performance.