2019/06/15 by Refilwe Kgoadi, C. A. Engelbrecht, Kgoadi, Refilwe +5
Engineering · Physics and Astronomy · #Astronomical Observations and Instrumentation #Astronomy and Astrophysical Research #FOS: Physical sciences #Gamma-ray bursts and supernovae #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Solar and Stellar Astrophysics (astro-ph.SR)
paper · pdf · doi:10.48550/arxiv.1906.06628
openalex publication_date 2019/06/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A significant degree of misclassification of variable stars through the application of machine learning methods to survey data motivates a search for more reliable and accurate machine learning procedures, especially in light of the very large data cubes that will be generated by future surveys and the need for immediate production of accurate, formalised catalogues of variable behaviour to enable science to proceed. In this study, the efficiency of an ensemble machine learning procedure utilising extreme boosting was determined by application to a large sample of data from the OGLE III and IV surveys and from the Kepler mission. Through recursive training of classifiers, the study developed a variable star classification workflow which produced an average efficiency determined with the average precision of the model (0.81 for Kepler and 0.91 for OGLE) and the f-score of predictions on the test sets. This suggests that extreme boosting can be presented as one of the favourable shallow learning methods in developing a variable star classifier for future large survey projects.