论文标题
通过数据增强的机器学习范围检测
Exoplanet Detection by Machine Learning with Data Augmentation
论文作者
论文摘要
最近已经证明，深度学习具有使用来自卫星的光曲线数据（例如Kepler \ cite {Borucki2010Kepler} \ cite {Koch2010Kepler}和NASA的Transiplanet Explanet调查卫星（Tess）{tess）{rickite {ricke2010。不幸的是，可用数据集的较小性使得很难意识到强大的网络体系结构所期望的性能水平。 在本文中，我们调查了从光曲线数据到训练神经网络以识别系外行星的数据增强技术的使用。所使用的增强技术分为两个类：简单（例如，增强噪声增强）和基于学习的（例如，首次培训gan \ cite {goodfellow2020 generative}以生成新示例）。我们证明，数据增强具有改善系外行星检测问题的模型性能的潜力，并建议随着更多的数据可用，建议基于生成模型的增强。
It has recently been demonstrated that deep learning has significant potential to automate parts of the exoplanet detection pipeline using light curve data from satellites such as Kepler \cite{borucki2010kepler} \cite{koch2010kepler} and NASA's Transiting Exoplanet Survey Satellite (TESS) \cite{ricker2010transiting}. Unfortunately, the smallness of the available datasets makes it difficult to realize the level of performance one expects from powerful network architectures. In this paper, we investigate the use of data augmentation techniques on light curve data from to train neural networks to identify exoplanets. The augmentation techniques used are of two classes: Simple (e.g. additive noise augmentation) and learning-based (e.g. first training a GAN \cite{goodfellow2020generative} to generate new examples). We demonstrate that data augmentation has a potential to improve model performance for the exoplanet detection problem, and recommend the use of augmentation based on generative models as more data becomes available.
