2017/07/13 by M. F. Pérez-Ortiz, Alejandro García-Varela, A. García-Varela +5 · 14 citations
Mathematics · Physics and Astronomy · #Advanced Statistical Methods and Models #Artificial intelligence #Astronomy and Astrophysical Research #Astrophysics #Computer science #Context (archaeology) #Data set #Fourier series #Fourier transform #Light curve #Mathematical analysis #Mathematics #Outlier #Pattern recognition (psychology) #Physics #Set (abstract data type) #Star (game theory) #Stars #Stellar classification #Stellar, planetary, and galactic studies #Variable star #astro-ph.IM
paper · pdf · doi:10.1051/0004-6361/201628937
published in Astronomy and Astrophysics 605, A123 (EDP Sciences)
openalex publication_date 2017/07/13 · arxiv created 2017/07/14 · arxiv updated 2017/09/20 · openalex created_date 2022/10/05 · openalex updated_date 2026/07/31
Context. Optical and infrared variability surveys produce a large number of high quality light curves. Statistical pattern recognition methods have provided competitive solutions for variable star classification at a relatively low computational cost. In order to perform supervised classification, a set of features is proposed and used to train an automatic classification system. Quantities related to the magnitude density of the light curves and their Fourier coefficients have been chosen as features in previous studies. However, some of these features are not robust to the presence of outliers and the calculation of Fourier coefficients is computationally expensive for large data sets.