Astronomy and Astrophysics · 2018 · 72 citations · 72 references
Natural SciencesAstronomyInfrared AstronomyPlanetary AstronomyPlanetary ScienceClassificationNear-infrared AstronomyPlanetary CompositionStar CatalogsTaxonomic ClassificationAsteroid Infrared ObservationsInfrared Photometric SurveysK-nearest NeighborsNear-infrared SurveysPhotometric ClassificationProbabilistic Taxonomic ClassificationTaxonomic AnnotationTaxonomic Classification Schemes
Context. The MOVIS catalog contains the largest set of near-infrared (NIR) colors for solar system objects. These data were obtained from the observations performed by VISTA-VHS survey using the Y , J , H , and Ks filters. The taxonomic classification of objects in this catalog allows us to obtain large-scale distributions for the asteroidal population, to study faint objects, and to select targets for detailed spectral investigations. Aims. We aim to provide a taxonomic classification for asteroids observed by VISTA-VHS survey. We derive a method for assigning a compositional type to an object based on its ( Y − J ), ( J − Ks ), and ( H − Ks ) colors. Methods. We present a taxonomic classification for 18 265 asteroids from the MOVIS catalog, using a probabilistic method and the k -nearest neighbors algorithm. Because our taxonomy is based only on NIR colors, several classes from Bus-DeMeo were clustered into groups and a slightly different notation was used: i.e., the superscript indicates that the classification was obtained based on the NIR colors and the subscript indicates possible misidentifications with other types. Our results are compared with the information provided by the Sloan Digital Sky Survey (SDSS) and Wide-field Infrared Survey Explorer (WISE). Results. The two algorithms used in this study give a taxonomic type for all objects having at least ( Y − J ) and ( J − Ks ) observed colors. A final classification is reported for a set of 6496 asteroids based on the criteria that k NN and probabilistic algorithms gave the same result, and the color errors are within the limits ( Y − J ) err ≤ 0.118 and ( J − Ks ) err ≤ 0.136. This set includes 144 bodies classified as B k ni , 613 as C ni , 197 as C gx ni , 91 as X t ni , 440 as D s ni , 665 as K l ni , 233 as A d ni , 3315 as S ni , and 798 as V ni . We report the albedo distribution for each taxonomic group and we compute new median values for the main types. We found that V-type and A-type candidates have identical size frequency distributions, but V types are five times more common than A types. Several particular cases, such as the A-type asteroid (11616) 1996 BQ2 and the S-type (3675) Kematsch, both in the Cybele population, are discussed.
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Scikit-learn: Machine Learning in Python
Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort et al. · arXiv (Cornell University) · 2012 · 63.7K citations · Full text
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F. E. DeMeo, Richard P. Binzel, Stephen M. Slivan et al. · Icarus · 2009 · 922 citations · Full text
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Amy Mainzer, J. M. Bauer, R. M. Cutri et al. · The Astrophysical Journal · 2014 · 752 citations · Full text