Astronomy16 papers
Artificial Intelligence and Machine Learning in Planetary Science
A companion collection for the ACM 2026 talk by Valerio Carruba
VC
Valerio Carruba
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AstronomyWorth Reading
intermediate
Machine learning-assisted dynamical classification of trans-Neptunian objects
Volk, Kathryn & Malhotra, Renu (2025)
- Published
- Jan 1, 2025
- Publisher
- Elsevier
- ISBN
- 9780443247705
- DOI
- 10.1016/b978-0-44-324770-5.00012-x
At a GlanceAI
Machine learning helps classify trans-Neptunian object dynamics more efficiently, aiding studies of the outer Solar System.
SummaryAI
The paper addresses the challenge of classifying the dynamical behavior of trans-Neptunian objects, which is important for understanding the structure and history of the outer Solar System. It introduces a machine learning-assisted approach to support this classification task, suggesting a faster or more scalable alternative to traditional analysis. By improving how these distant objects are sorted into dynamical categories, the work could help researchers better interpret populations beyond Neptune and refine models of Solar System evolution.
Method SnapshotAI
The study uses a machine learning-assisted approach for dynamical classification of trans-Neptunian objects.
BackgroundAI
Basic knowledge of Solar System dynamics, trans-Neptunian objects, and machine learning concepts is helpful.
One of the early applications of machine learning to the dynamical classification of trans-Neptunian objects, this work illustrates how AI can streamline the analysis of complex orbital behavior. Its methodology remains relevant as modern surveys continue to expand the known population of distant Solar System bodies, making automated classification increasingly important.
— VC