Detecting Exoplanet Transits through Machine-learning Techniques with Convolutional Neural Networks

被引：11
|
作者：
Chintarungruangchai, Pattana [1 ,2 ]
Jiang, Ing-Guey [1 ,2 ]
机构：
[1] Natl Tsing Hua Univ, Dept Phys, Hsinchu, Taiwan
[2] Natl Tsing Hua Univ, Inst Astron, Hsinchu, Taiwan
来源：
PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC | 2019年 / 131卷 / 1000期
关键词：
planets and satellites: detection; methods: numerical; KEPLER;
D O I：
10.1088/1538-3873/ab13d3
中图分类号：
P1 [天文学];
学科分类号：
0704 ;
摘要：
A machine-learning technique with two-dimension convolutional neural network is proposed for detecting exoplanet transits. To test this new method, five different types of deep-learning models with or without folding are constructed and studied. The light curves of the Kepler Data Release 25 are employed as the input of these models. The accuracy, reliability, and completeness are determined and their performances are compared. These results indicate that a combination of two-dimension convolutional neural network with folding would be an excellent choice for the future transit analysis.
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收藏
页数：15
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