Identifying Exoplanets with Deep Learning: A Five Planet Resonant Chain around Kepler-80 and an Eighth Planet around Kepler-90
Christopher J. Shallue, Andrew Vanderburg

TL;DR
This paper introduces a deep learning method to classify exoplanet signals, successfully validating two new planets, including an eighth planet around Kepler-90, demonstrating the approach's effectiveness in exoplanet detection.
Contribution
The paper presents a novel deep convolutional neural network for classifying exoplanet signals, achieving high accuracy and enabling the discovery of new planets in Kepler data.
Findings
Achieved 98.8% ranking accuracy for planet signals
Validated two new planets, including Kepler-90's eighth planet
Discovered a five-planet resonant chain around Kepler-80
Abstract
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…
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