A survey on machine learning based light curve analysis for variable astronomical sources. (4th July 2021)

• Record Type: Journal Article
• Authors: Yu, Ce; Li, Kun; Zhang, Yanxia; Xiao, Jian; Cui, Chenzhou; Tao, Yihan; Tang, Shanjiang; Sun, Chao; Bi, Chongke
• Is Part Of: Wiley interdisciplinary reviews. Volume 11:Number 5(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-07-04
• Subjects: deep learning -- light curve analysis -- machine learning -- variable; Data mining -- Periodicals; 006.31205
• DOI: 10.1002/widm.1425
• Languages: English
• ISSNs: 1942-4787

Abstract

Abstract: The improvement of observation capabilities has expanded the scale of new data available for time domain astronomy research, and the accumulation of observational data continues to accelerate. However, traditional data analysis methods are difficult to fully tap the potential scientific value of all data. Therefore, in the current and future research on light curve analysis, it is inevitable to use artificial intelligence (AI) technology to assist in data analysis in order to obtain as many candidates as possible with scientific research goals. This survey reviews important developments in light curve analysis over the past years, summarizes the basic concepts in machine learning and their applications in light curve analysis and concludes perspectives and challenges for light curve analysis in the near future. The full exploration of light curves of variable celestial objects relies heavily on new techniques derived from promotion of machine learning and deep learning in the astronomical big data era. This article is categorized under: Technologies > Machine Learning Technologies > Artificial Intelligence Abstract : Machine learning based light curve analysis