We present here multiband photometry for over 2000 Main-Belt asteroids. For each target, we report the probabilistic taxonomy using the measured V – R and V – I colors in combination with a machine-learning-generated decision surface in color–color space. Through this method, we classify >85% of our targets as one the four main Bus–DeMeo complexes: S-, C-, X-, or D-type. Roughly one-third of our targets have a known associated dynamic family, with 69 families represented in our data. Within uncertainties, our results show no discernible difference in taxonomic distribution between family members and non-family members. Nine of the sixty-nine families represented in our observed sample had 20 or more members present, and therefore, we investigate the taxonomy of these families in more detail and find excellent agreement with the literature. Out of these nine well-sampled families, our data show that the Themis, Koronis, Hygiea, Massalia, and Eunomia families display a high degree of taxonomic homogeneity and that the Vesta, Flora, Nysa–Polana, and Eos families show a significant level of mixture in taxonomies. Using the taxonomic purity and the degree of dispersion in observed colors for each of the nine well-sampled collisional families, we also speculate which of these families potentially originated from a differentiated parent body and/or is a family with a possible undetermined nested family. In addition, we obtained sufficient photometric data for 433 of our targets to extract reliable rotation periods and observe no obvious correlation between rotation properties and family membership.
- Minor planets, asteroids: individual (Main-Belt asteroids)
- Techniques: photometric Supporting material: figure set, machine-readable table
ASJC Scopus subject areas
- Astronomy and Astrophysics
- Space and Planetary Science
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Erasmus, N. (Creator), McNeill, A. (Creator), Mommert, M. (Creator), Trilling, D. E. (Creator), Sickafoose, A. A. (Creator), Paterson, K. (Creator) & Trilling, D. E. (Creator), Strasbourg Astronomical Data Center, 2020