Asteroid families classification: Exploiting very large datasets
Andrea Milani et al. (2014)
- Published
- Sep 1, 2014
- Journal
- Icarus · Vol. 239
- DOI
- 10.1016/j.icarus.2014.05.039
At a GlanceAI
A scalable framework to classify asteroid families in very large datasets, improving dynamical context for resonant populations.
SummaryAI
The work addresses how to classify asteroid families when modern surveys produce extremely large catalogs, where traditional workflows become hard to apply reliably. It proposes a classification strategy designed to scale with dataset size, aiming to better separate true collisional families from unrelated background objects. This matters because cleaner family membership is crucial for interpreting the distribution of asteroids near mean-motion resonances and for disentangling collisional structure from resonance-driven transport. The result is a more robust starting point for studies linking family evolution, resonance sticking, and delivery pathways across the main belt.
Method SnapshotAI
Large-scale asteroid family identification and classification using a dataset-driven, scalable clustering/validation workflow.
BackgroundAI
Celestial mechanics of the asteroid belt, mean-motion resonances, and basic familiarity with asteroid family concepts and proper elements.