Machine Learning applied to asteroid dynamics

Abstract

Machine Learning (ML) is the branch of computer science that studies computer algorithms that can learn from data. It is mainly divided into supervised learning, where the computer is presented with examples of entries, and the goal is to learn a general rule that maps inputs to outputs, and unsupervised learning, where no label is provided to the learning algorithm, leaving it alone to find structures. Deep learning is a branch of machine learning based on numerous layers of artificial neural networks, which are computing systems inspired by the biological neural networks that constitute animal brains. In asteroid dynamics, machine learning methods have been recently used to identify members of asteroid families, and to identify resonant arguments images of asteroids in three-body resonances, among other applications. Here, we will conduct a review of available literature in the field, and classify it in terms of metrics recently used by other authors to assess the state of the art of applications of machine learning in other astronomical subfields. For comparison, applications of machine learning to Solar System bodies, a larger area that includes imaging and spectrophotometry of small bodies, have already reached a state classified as progressing. Research communities and methodologies are more established, and the use of ML led to the discovery of new celestial objects or features. ML applied to asteroid dynamics, however, is still in the emerging phase, with smaller groups, and fewer papers producing discoveries. Large observational surveys, like those conducted at the Vera C. Rubin Observatory, will produce very substantial datasets of orbital and physical properties for asteroids. Applications of ML for clustering, image identification, and anomaly detection, among others, are currently being developed and are expected of being of great help.

20 pages, 15 figures, 2 tables. Review paper on the status of applications of Machine learning to asteroid dynamics. Accepted for publication by Celestial Mechanics and Dynamical Astronomy (CMDA), comments are welcome

References in corpus (25)

• Scikit-learn: Machine Learning in Python
• XGBoost: A Scalable Tree Boosting System
• BART: Bayesian additive regression trees
• Data Mining and Machine Learning in Astronomy
• The Zwicky Transient Facility: Observing System
• Formation of asteroid pairs by rotational fission
• Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning
• Machine Learning for the Zwicky Transient Facility
• Machine Learning in Astronomy: a practical overview
• Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
• Gaia Data Release 2: Observations of solar system objects
• Autoregressive Times Series Methods for Time Domain Astronomy
• DeepStreaks: identifying fast-moving objects in the Zwicky Transient Facility data with deep learning
• Asteroid Discovery and Characterization with the Large Synoptic Survey Telescope (LSST)
• Characterization of Near-Earth Asteroids using KMTNet-SAAO
• The Data Big Bang and the Expanding Digital Universe: High-Dimensional, Complex and Massive Data Sets in an Inflationary Epoch
• First Results from the Rapid-Response Spectrophotometric Characterization of Near-Earth Objects using UKIRT
• Taxonomy and Light-Curve Data of 1000 Serendipitously Observed Main-Belt Asteroids
• Artificial Neural Network classification of asteroids in the M1:2 mean-motion resonance with Mars
• Machine Learning Based Real Bogus System for HSC-SSP Moving Object Detecting Pipeline
• Machine Learning Classification of Kuiper Belt Populations
• Searching for Moving Objects in HSC-SSP: Pipeline and Preliminary Results
• Machine learning prediction for mean motion resonance behaviour -- The planar case
• Influence of Apophis' spin axis variations on a spacecraft during the 2029 close approach with Earth
• Probabilistic modeling of asteroid diameters from Gaia DR2 errors

Cited by in corpus (4)

• Asteroid Families: properties, recent advances and future opportunities
• Using neural networks to model Main Belt Asteroid albedos as a function of their proper orbital elements
• Large-step neural network for learning the symplectic evolution from partitioned data
• Long-Term Dynamical Evolution and Ejection of Near-Earth Asteroids