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While proper orbital elements are currently available for more than 1 millionasteroids, taxonomical information is still lagging behind. Surveys likeSDSS-MOC4 provided preliminary information for more than 100,000 objects, butmany asteroids still lack even a basic taxonomy. In this study, we use DarkEnergy Survey (DES) data to provide new information on asteroid physicalproperties. By cross-correlating the new DES database with other databases, weinvestigate how asteroid taxonomy is reflected in DES data. While theresolution of DES data is not sufficient to distinguish between differentasteroid taxonomies within the complexes, except for V-type objects, it canprovide information on whether an asteroid belongs to the C- or S-complex.Here, machine learning methods optimized through the use of genetic algorithmswere used to predict the labels of more than 68,000 asteroids with no priortaxonomic information. Using a high-quality, limited set of asteroids with dataon $gri$ slopes and $i-z$ colors, we detected 409 new possible V-typeasteroids. Their orbital distribution is highly consistent with that of otherknown V-type objects.

Main belt asteroids taxonomical information from Dark Energy Survey data