Characterization of Surface Heterogeneity among Asteroid Taxonomic Classes according to Sloan Digital Sky Survey Observations

This research characterizes the extent of surface heterogeneity among asteroid classes by the extent of Sloan Digital Sky Survey (SDSS) color variance within multiple observations of the same asteroid. The SDSS MOC4 database includes data from 220,101 observations of 104,449 unique objects. The amount of multiple observations of one target makes it ideal for statistically analyzing the surface inhomogeneity of asteroid surfaces. Information from the SDSS MOC4 database (below an error threshold determined from standard error propagation techniques and the interquartile range) is combined with information from the classification in Carvano et al. (2010) to analyze asteroid surface heterogeneity based on taxonomic class. Individual observations are grouped by asteroid, and asteroids are grouped by class. The standard deviation of each normalized SDSS color (i.e. u-r, g-r, r-i, r-z) for each asteroid with multiple observations is calculated. The mean of the standard deviations is then computed for a given class. Comparison of the size of the average standard deviation to the size of the error determines the extent of true variance within a normalized color in a class. The effect of phase angles on SDSS data, as discussed in Carvano et al. (2015), are considered. Additionally, implications for space weathering and evolutionary relationships between taxonomic classes are explored.