Identifying Exoplanets with Deep Learning: A Five-planet Resonant Chain around Kepler-80 and an Eighth Planet around Kepler-90
By Christopher J. Shallue, Andrew Vanderburg. 2017
323 citations recorded by the source corpus.
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 that 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.
Subfields: Astronomy and Astrophysics, Instrumentation
View Paper: https://www.semanticscholar.org/paper/d345dc4a375e64b5baf5747bc4e2c2edf87a95c7
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Citations in the corpus
- Optimized transit detection algorithm to search for periodic transits of small planets (2019, background): “However, no false/true positive/negative rates are given (as in our Figure 6), preventing a comparison to TLS. Training times for algorithms based on deep-learning to detect transits are of order several thousand CPU hours (Shallue & Vanderburg 2018).”
- The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting (2019, methodology): “…of astronomy and astrophysics are the automatic identification of exoplanets from the Kepler catalog (Kielty et al., 2018; Pearson et al., 2017; Shallue & Vanderburg, 2018), the analysis of stellar spectra from Gaia (Fabbro et al., 2017; Li et al., 2017), and the detection of gravitational…”