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。