Reconstruct light curves from unevenly sampled variability data with artificial neural networks

	Reconstruct light curves from unevenly sampled variability data with artificial neural networks
	Wang, Qi-Jie 1; Cao, Xinwu2
刊名	ASTROPHYSICS AND SPACE SCIENCE
	2014-07-01
卷号	352 期号:1 页码:51-55
关键词	Galaxies: active Galaxies: jets BL Lacertae objects: general
通讯作者	曹新伍
英文摘要	Light curves are usually constructed from discrete observational data by interpolation. In most cases, the observation data is temporally uneven, and therefore the light curve is usually derived by the interpolation of the binned data with the spline function, which is intended for reducing the "high sample noise" (i.e., the variability in the timescales comparable with the bin width). Such a practice of course reduces the time resolution of the light curve. It is known that function approximation is one of the most important applications of the artificial neural networks (ANN). In this work, for the first time we tentatively use the ANN to construct light curves from unevenly sampled variability data. To demonstrate the advantages of ANN for signal reconstruction over commonly used cubic spline function scheme, two sets of simulated periodic functions are used with random noises of varying magnitudes, one single frequency based and one multiple (two) frequency based. These signal reconstruction tests show that the ANN is clearly superior to the cubic spline scheme. As a case study, we use the uneven long-term multi-band monitoring data of BL lacertae to derive the light curves with ANN. It is found that the light curves derived with ANN have higher time resolution than those with the cubic spline function adopted in previous works. We recommend using ANN for the signal reconstruction in astrophysical data analysis as well as that of in other fields.
WOS标题词	Science & Technology ; Physical Sciences
类目[WOS]	Astronomy & Astrophysics
研究领域[WOS]	Astronomy & Astrophysics
关键词[WOS]	BLAZAR 3C 279 ; BL LACERTAE ; MULTIWAVELENGTH OBSERVATIONS ; OPTICAL OBSERVATIONS
收录类别	SCI
语种	英语
WOS记录号	WOS:000336975200006
公开日期	2015-02-14
内容类型	期刊论文
源URL	[http://119.78.226.72//handle/331011/15712]
专题	上海天文台_星系宇宙学重点实验室
作者单位	1.Cent S Univ, Sch Geosci & Infophys, Changsha 410083, Peoples R China 2.Chinese Acad Sci, Shanghai Astron Observ, Key Lab Res Galaxies & Cosmol, Shanghai 200030, Peoples R China
推荐引用方式 GB/T 7714	Wang, Qi-Jie,Cao, Xinwu. Reconstruct light curves from unevenly sampled variability data with artificial neural networks[J]. ASTROPHYSICS AND SPACE SCIENCE,2014,352(1):51-55.
APA	Wang, Qi-Jie,&Cao, Xinwu.(2014).Reconstruct light curves from unevenly sampled variability data with artificial neural networks.ASTROPHYSICS AND SPACE SCIENCE,352(1),51-55.
MLA	Wang, Qi-Jie,et al."Reconstruct light curves from unevenly sampled variability data with artificial neural networks".ASTROPHYSICS AND SPACE SCIENCE 352.1(2014):51-55.