  Most recently, machine learning has been used to study the dynamics of
integrable Hamiltonian systems and the chaotic 3-body problem. In this work, we
consider an intermediate case of regular motion in a non-integrable system: the
behaviour of objects in the 2:3 mean motion resonance with Neptune. We show
that, given initial data from a short 6250 yr numerical integration, the
best-trained artificial neural network (ANN) can predict the trajectories of
the 2:3 resonators over the subsequent 18750 yr evolution, covering a full
libration cycle over the combined time period. By comparing our ANN's
prediction of the resonant angle to the outcome of numerical integrations, the
former can predict the resonant angle with an accuracy as small as of a few
degrees only, while it has the advantage of considerably saving computational
time. More specifically, the trained ANN can effectively measure the resonant
amplitudes of the 2:3 resonators, and thus provides a fast approach that can
identify the resonant candidates. This may be helpful in classifying a huge
population of KBOs to be discovered in future surveys.
