Machine learning prediction for mean motion resonance behaviour -- The planar case

Abstract

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.

arXiv comment12 pages, 9 figures, accepted for pubblication on Monthly Notices of the Royal Astronomical Society

References in corpus (31)

• LSST: from Science Drivers to Reference Design and Anticipated Data Products
• THE ORIGIN OF PLUTO'S ORBIT: IMPLICATIONS FOR THE SOLAR SYSTEM BEYOND NEPTUNE
• Medical Image Retrieval using Deep Convolutional Neural Network
• Neptune's Migration into a Stirred-Up Kuiper Belt: A Detailed Comparison of Simulations to Observations
• Neptune's Orbital Migration Was Grainy, Not Smooth
• The Phase Space Structure Near Neptune Resonances in the Kuiper Belt
• AI Poincaré: Machine Learning Conservation Laws from Trajectories
• Predicting the long-term stability of compact multiplanet systems
• Newton vs the machine: solving the chaotic three-body problem using deep neural networks
• A Machine Learns to Predict the Stability of Tightly Packed Planetary Systems
• OSSOS III - Resonant Trans-Neptunian Populations: Constraints from the first quarter of the Outer Solar System Origins Survey
• Painting galaxies into dark matter halos using machine learning
• Machine-learning Approaches to Exoplanet Transit Detection and Candidate Validation in Wide-field Ground-based Surveys
• The miniJPAS survey: star-galaxy classification using machine learning
• Machine learning technique for morphological classification of galaxies from the SDSS. I. Photometry-based approach
• A machine learns to predict the stability of circumbinary planets
• An interpretable machine learning framework for dark matter halo formation
• Exoplanet Validation with Machine Learning: 50 new validated Kepler planets
• OSSOS: XIII. Fossilized Resonant Dropouts Tentatively Confirm Neptune's Migration was Grainy and Slow
• Trans-Neptunian Objects Transiently Stuck in Neptune's Mean Motion Resonances: Numerical simulations of the current population
• Dynamical Classification of Trans-Neptunian Objects Detected by the Dark Energy Survey
• Artificial Neural Network classification of asteroids in the M1:2 mean-motion resonance with Mars
• A study of the high-inclination population in the Kuiper belt -- 1. The Plutinos
• Details of Resonant Structures Within a Nice Model Kuiper Belt: Predictions for High-Perihelion TNO Detections
• Machine Learning Classifiers for Intermediate Redshift Emission Line Galaxies
• Machine-z: Rapid Machine Learned Redshift Indicator for Swift Gamma-ray Bursts
• Machine Learning Classification of Kuiper Belt Populations
• TSARDI: a Machine Learning data rejection algorithm for transiting exoplanet light curves
• A study of the high-inclination population in the Kuiper belt -- III. The 4:7 mean motion resonance
• A machine learning approach for GRB detection in AstroSat CZTI data
• Machine Learning the Fates of Dark Matter Subhalos: A Fuzzy Crystal Ball

Cited by in corpus (5)

• Machine Learning applied to asteroid dynamics
• Deep learning in bifurcations of particle trajectories
• A study of the high-inclination population in the Kuiper belt -- IV. High-order mean motion resonances in the classical region
• Large-step neural network for learning the symplectic evolution from partitioned data
• A new multiple-arc model of the resonant Kuiper belt objects -- Plutinos