Optimization of Future Multifilter Surveys Toward Asteroid Characterization

The aim of this paper is to find a set of photometric passbands that will give optimal results for spectrophotometric classification of asteroids into taxonomic types and classes. For this purpose various machine-learning methods are used, namely multinomial logistic regression, naive Bayes, support vector machines, gradient boosting, and multilayer perceptrons. Sequential feature selection is performed to assess the contribution of each reflectance difference. We find that to determine the taxonomic complexes with a balanced accuracy of 85%, a set of five spectrophotometric bands is required. For taxonomy type determination with the balanced accuracy of 80% a set of eight bands is necessary. Furthermore, only a three-band system is enough for distinguishing the C-complex asteroids with 92% balanced accuracy. These results can be used for designing future asteroid multifilter sky surveys.

Automated summary

The aim of this paper is to find a set of photometric passbands for accurate spectrophotometric classification of asteroids. Various machine-learning methods are used and a sequential feature selection is performed. The results suggest that a set of five bands is required for determining the taxonomic complexes, eight bands for taxonomy type determination, and just three bands for distinguishing C-complex asteroids. These findings have important implications for the design of future asteroid sky surveys.