NASA's Kepler Space Telescope has been instrumental in the task of finding
the presence of exoplanets in our galaxy. This search has been supported by
computational data analysis to identify exoplanets from the signals received by
the Kepler telescope. In this paper, we consider building upon some existing
work on exoplanet identification using residual networks for the data of the
Kepler space telescope and its extended mission K2. This paper aims to explore
how deep learning algorithms can help in classifying the presence of exoplanets
with less amount of data in one case and a more extensive variety of data in
another. In addition to the standard CNN-based method, we propose a Siamese
architecture that is particularly useful in addressing classification in a
low-data scenario. The CNN and ResNet algorithms achieved an average accuracy
of 68% for three classes and 86% for two-class classification. However, for
both the three and two classes, the Siamese algorithm achieved 99% accuracy.

本文探讨了如何使用深度学习算法和 Siamese 结构将 Kepler 太空望远镜以及其扩展任务 K2 的数据进行分类，结果表明 Siamese 算法在低数据情况下表现良好，并可用于识别恒星周围行星的存在。

利用机器学习技术识别和分类系外行星

Identification and Classification of Exoplanets Using Machine Learning  Techniques

Exoplanet detection opens the door to the discovery of new habitable worlds
and helps us understand how planets were formed. With the objective of finding
earth-like habitable planets, NASA launched Kepler space telescope and its
follow up mission K2. The advancement of observation capabilities has increased
the range of fresh data available for research, and manually handling them is
both time-consuming and difficult. Machine learning and deep learning
techniques can greatly assist in lowering human efforts to process the vast
array of data produced by the modern instruments of these exoplanet programs in
an economical and unbiased manner. However, care should be taken to detect all
the exoplanets precisely while simultaneously minimizing the misclassification
of non-exoplanet stars. In this paper, we utilize two variations of generative
adversarial networks, namely semi-supervised generative adversarial networks
and auxiliary classifier generative adversarial networks, to detect transiting
exoplanets in K2 data. We find that the usage of these models can be helpful
for the classification of stars with exoplanets. Both of our techniques are
able to categorize the light curves with a recall and precision of 1.00 on the
test data. Our semi-supervised technique is beneficial to solve the cumbersome
task of creating a labeled dataset.

本研究使用半监督生成对抗网络和辅助分类器生成对抗网络来检测 K2 数据中的瞬变系外行星，发现这些模型的使用对于瞬变系外行星的辨别非常有帮助，同时我们的技术可提高数据分类的效率，解决数据标记的困难问题。

ExoSGAN 和 ExoACGAN：使用对抗训练算法进行太阳系外行星探测

ExoSGAN and ExoACGAN: Exoplanet Detection using Adversarial Training Algorithms

NASA's Kepler Space Telescope was designed to determine the frequency of
Earth-sized planets orbiting Sun-like stars, but these planets are on the very
edge of the mission's detection sensitivity. Accurately determining the
occurrence rate of these planets will require automatically and accurately
assessing the likelihood that individual candidates are indeed planets, even at
low signal-to-noise ratios. We present a method for classifying potential
planet signals using deep learning, a class of machine learning algorithms that
have recently become state-of-the-art in a wide variety of tasks. We train a
deep convolutional neural network to predict whether a given signal is a
transiting exoplanet or a false positive caused by astrophysical or
instrumental phenomena. Our model is highly effective at ranking individual
candidates by the likelihood that they are indeed planets: 98.8% of the time it
ranks plausible planet signals higher than false positive signals in our test
set. We apply our model to a new set of candidate signals that we identified in
a search of known Kepler multi-planet systems. We statistically validate two
new planets that are identified with high confidence by our model. One of these
planets is part of a five-planet resonant chain around Kepler-80, with an
orbital period closely matching the prediction by three-body Laplace relations.
The other planet orbits Kepler-90, a star which was previously known to host
seven transiting planets. Our discovery of an eighth planet brings Kepler-90
into a tie with our Sun as the star known to host the most planets.

文章介绍了使用深度学习对开普勒探测器发现的潜在系外行星信号进行分类鉴别的方法，并应用到已知多行星系统中发现两个新的行星，其中一个则是在 Kepler-80 周围的五个行星的 3 体 - 拉普拉斯共振链中。