Digitally filtered resonant arguments for deep learning classification of asteroids in secular resonances

Abstract

Node secular resonances, or s-type secular resonances, occur when the precession frequencies of the node of an asteroid and some planets are in commensurability. They are important for changing the proper inclination of asteroids interacting with them. Traditionally, identifying the asteroid resonant status was mostly performed by visual inspection of plots of the time series of the asteroid resonant argument to check for oscillations around an equilibrium point. Recently, deep learning methods based on convolutional neural networks (CNNs) for the automatic classification of images have become more popular for these kinds of tasks, allowing for the classification of thousands of orbits in a few minutes. In this work, we study 11 s-type resonances in the asteroid main belt and in the Hungaria region and focus on the four most diffusive ones. Two secular resonances in the Hungaria region, the 2 . s - s(4) - s(6) and the s - 2 . s(6) + s(7) - g(6) + g(8) overlap, but this has negligible effects in terms of chaotic dynamics. Here, we obtained filtered images of the resonant arguments by filtering out all low-frequency signals with a Butterworth filter. A simple method based on amplitudes and periods of librations can perform a preliminary selection of asteroids in librating orbits. Our results show that CNN models applied to filtered images are much more effective in terms of metrics like accuracy, Precision, Recall, and F1-score than those that use images of osculating resonant arguments. Filtered resonant arguments should be preferentially used to identify asteroids interacting with secular resonances.