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Iranian Journal of Astronomy and Astrophysic، جلد ۹، شماره ۱، صفحات ۳۱-۴۴
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عنوان انگلیسی Hierarchical Classification of Variable Stars Using Deep Convolutional Neural Networks
چکیده انگلیسی مقاله The importance of using fast and automatic methods to classify variable stars for large amounts of data is undeniable. There have been many attempts to classify variable stars by traditional algorithms like Random Forest. In recent years, neural networks as classifiers have come to notice because of their lower computational cost compared to traditional algorithms. This paper uses the Hierarchical Classification technique, which contains two main steps of predicting class and then subclass of stars. All the models in both steps have same network structure and we test both Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Our pre-processing method uses light curves and period of stars as input data. We consider most of the classes and subclasses of variable stars in OGLE-IV database and show that using Hierarchical Classification technique and designing appropriate preprocessing can increase accuracy of predicting smaller classes, ACep and T2Cep. We obtain an accuracy of 98% for class classification and 93% for subclasses classification.
کلیدواژه‌های انگلیسی مقاله Variable Stars, Hierarchical Method, Convolutional Neural Networks
نویسندگان مقاله Mahdi Abdollahi |
School of Astronomy, Institute for Research in Fundamental Sciences (IPM)

Nooshin Torabi |
Department of Physics, Sharif University of Technology

Sadegh Raeisi |
Department of Physics, Sharif University of Technology

Sohrab Rahvar |
Department of Physics, Sharif University of Technology

نشانی اینترنتی https://ijaa.du.ac.ir/article_302_c2901d4f6a6dc82f720b22a07e388167.pdf
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