Comparison of machine learning algorithms used to classify the asteroids observed by all-sky surveys

Context. Multifilter photometry from large sky surveys is commonly used to assign asteroid taxonomic types and study various problems in planetary science. To maximize the science output of those surveys, it is important to use methods that best link the spectro-photometric measurements to asteroid taxonomy. Aims. We aim to determine which machine learning methods are the most suitable for the taxonomic classification for various sky surveys. Methods. We utilized five machine learning supervised classifiers: logistic regression, naive Bayes, support vector machines (SVMs), gradient boosting, and MultiLayer Perceptrons (MLPs). Those methods were found to reproduce the Bus-DeMeo taxonomy at various rates depending on the set of filters used by each survey. We report several evaluation metrics for a comprehensive comparison (prediction accuracy, balanced accuracy, F1 score, and the Matthews correlation coefficient) for 11 surveys and space missions. Results. Among the methods analyzed, multilayer perception and gradient boosting achieved the highest accuracy and naive Bayes achieved the lowest accuracy in taxonomic prediction across all surveys. We found that selecting the right machine learning algorithm can improve the success rate by a factor of >2. The best balanced accuracy (similar to 85% for a taxonomic type prediction) was found for the Visible and Infrared Survey telescope for Astronomy (VISTA) and the ESA Euclid mission surveys where broadband filters best map the 1 mu m and 2 mu m olivine and pyroxene absorption bands. Conclusions. To achieve the highest accuracy in the taxonomic type prediction based on multifilter photometric measurements, we recommend the use of gradient boosting and MLP optimized for each survey. This can improve the overall success rate even when compared with naive Bayes. A merger of different datasets can further boost the prediction accuracy. For the combination of the Legacy Survey of Space and Time and VISTA survey, we achieved 90% for the taxonomic type prediction.Peer reviewe

Asteroid phase curves using sparse Gaia DR2 data and differential dense light curves

The amount of sparse asteroid photometry being gathered by both space- and ground-based surveys is growing exponentially. This large volume of data poses a computational challenge owing to both the large amount of information to be processed and the new methods needed to combine data from different sources (e.g. obtained by different techniques, in different bands, and having different random and systematic errors). The main goal of this work is to develop an algorithm capable of merging sparse and dense data sets, both relative and differential, in preparation for asteroid observations originating from, for example, Gaia, TESS, ATLAS, LSST, K2, VISTA, and many other sources. We present a novel method to obtain asteroid phase curves by combining sparse photometry and differential ground-based photometry. In the traditional approach, the latter cannot be used for phase curves. Merging those two data types allows for the extraction of phase-curve information for a growing number of objects. Our method is validated for 26 sample asteroids observed by the Gaia mission.Comment: 10 pages, 7 figures (supplementary material: 20 pages, 58 figures