Generating Transit Light Curves with Variational Autoencoders

被引：3

作者：

Woodward, Douglas ^{[1
]}

Stevens, Elizabeth ^{[1
]}

Linstead, Erik ^{[1
]}

机构：

[1] Chapman Univ, Schmid Coll Sci & Technol, Machine Learning & Assist Technol Lab, Orange, CA 92866 USA

来源：

2019 IEEE INTERNATIONAL CONFERENCE ON SPACE MISSION CHALLENGES FOR INFORMATION TECHNOLOGY (SMC-IT 2019) | 2019年

关键词：

machine learning; variational autoencoders; neural networks; satellites; astronomy; extrasolar planets; ERROR-CORRECTION;

D O I：

10.1109/SMC-IT.2019.00008

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

We leverage variational autoencoders to generate transient light curves of distant exoplanets and stars in order to demonstrate the efficacy of deep learning techniques for this class of data. The ability to generate accurate light curves with desirable characteristics becomes more and more necessary with the success of recent astronomical missions and upcoming missions and will be a key enabler of the development of future models and research. The first study of its type to date, our initial results indicate a promising new research direction worthy of further development.

引用

页码：24 / 32

页数：9

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