Photometric light curves classification with machine learning
This addresses the need for efficient classification of massive astronomical data for astronomers, but it is incremental as it applies existing machine learning techniques to a specific domain challenge.
The authors tackled the problem of classifying astronomical light curves from the Large Synoptic Survey Telescope using an automated method based on gradient boosting, feature extraction, and augmentation, achieving a top result in the PLAsTiCC challenge.
The Large Synoptic Survey Telescope will complete its survey in 2022 and produce terabytes of imaging data each night. To work with this massive onset of data, automated algorithms to classify astronomical light curves are crucial. Here, we present a method for automated classification of photometric light curves for a range of astronomical objects. Our approach is based on the gradient boosting of decision trees, feature extraction and selection, and augmentation. The solution was developed in the context of The Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC) and achieved one of the top results in the challenge.