Astronomy16 papers
Artificial Intelligence and Machine Learning in Planetary Science
A companion collection for the ACM 2026 talk by Valerio Carruba
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Valerio Carruba
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Identification of asteroids trapped inside three-body mean motion resonances: a machine-learning approach
Smirnov, Evgeny A. & Markov, Alexey B. (2017)
- Published
- Apr 26, 2017
- Journal
- Monthly Notices of the Royal Astronomical Society · Vol. 469 · No. 2
- DOI
- 10.1093/mnras/stx999
At a GlanceAI
Machine learning helps identify asteroids trapped in complex three-body orbital resonances.
SummaryAI
The paper addresses the difficult task of finding asteroids caught in three-body mean motion resonances, which are subtle gravitational relationships involving multiple bodies. Its novelty is the use of a machine-learning approach to automate or improve this identification problem. That matters because better resonance classification can strengthen studies of asteroid dynamics and the long-term structure of the Solar System.
Method SnapshotAI
The authors use a machine-learning approach to identify asteroids in three-body mean motion resonances.
BackgroundAI
Basic background in asteroid dynamics, orbital resonances, and machine learning is helpful.
A pioneering application of machine learning to asteroid dynamics, this work showed that scikit-learn classifiers could successfully identify three-body mean motion resonances. While the field has since advanced considerably, the paper remains a useful reference for researchers interested in applying classical machine-learning techniques to problems in celestial mechanics.
— VC