  While proper orbital elements are currently available for more than 1 million
asteroids, taxonomical information is still lagging behind. Surveys like
SDSS-MOC4 provided preliminary information for more than 100,000 objects, but
many asteroids still lack even a basic taxonomy. In this study, we use Dark
Energy Survey (DES) data to provide new information on asteroid physical
properties. By cross-correlating the new DES database with other databases, we
investigate how asteroid taxonomy is reflected in DES data. While the
resolution of DES data is not sufficient to distinguish between different
asteroid taxonomies within the complexes, except for V-type objects, it can
provide information on whether an asteroid belongs to the C- or S-complex.
Here, machine learning methods optimized through the use of genetic algorithms
were used to predict the labels of more than 68,000 asteroids with no prior
taxonomic information. Using a high-quality, limited set of asteroids with data
on $gri$ slopes and $i-z$ colors, we detected 409 new possible V-type
asteroids. Their orbital distribution is highly consistent with that of other
known V-type objects.
