Astronomy16 papers
Artificial Intelligence and Machine Learning in Planetary Science
A companion collection for the ACM 2026 talk by Valerio Carruba
VC
Valerio Carruba
All Reviews
AstronomyWorth Reading
intermediate
Machine-learning identification of asteroid groups
Carruba, V. et al. (2019)
- Published
- Jul 1, 2019
- Journal
- Monthly Notices of the Royal Astronomical Society · Vol. 488 · No. 1
- DOI
- 10.1093/mnras/stz1795
At a GlanceAI
Machine learning improves asteroid family detection, recovering known groups and revealing new candidate families and clumps.
SummaryAI
Asteroid families help scientists reconstruct the Solar System's collisional history, so better ways to identify them are valuable. This study applies supervised machine-learning hierarchical clustering to asteroid family identification and finds that it matches traditional methods well, with high accuracy and strong recovery of previously known family members. It also reports 6 new families and 13 new clumps that appear physically and taxonomically consistent. The results suggest machine-learning clustering can be a fast and effective tool for expanding and refining asteroid group catalogs.
Method SnapshotAI
The study uses supervised machine-learning hierarchical clustering to identify asteroid families in proper-element space.
BackgroundAI
Basic knowledge of asteroid families, orbital dynamics, and introductory machine-learning classification concepts is helpful.
A pioneering application of machine learning to asteroid family identification, this work showed that supervised clustering algorithms can efficiently recover known families and identify new candidate groups. The paper remains a valuable reference for understanding how machine-learning methods can complement traditional HCM approaches, particularly as the size of asteroid catalogs continues to increase.
— VC