2019/09/10 by Tatiana Gabruseva, Gabruseva, Tatiana, Sergey Zlobin +3
Computer Science · Engineering · Mathematics · Physics and Astronomy · #Astronomical Observations and Instrumentation #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Computer and information sciences #FOS: Physical sciences #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Stellar, planetary, and galactic studies #Time Series Analysis and Forecasting #astro-ph.CO #astro-ph.IM #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1909.05032
arxiv created 2019/09/10 · openalex publication_date 2019/09/10 · arxiv updated 2019/09/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.