Single Transit Detection in Kepler with Machine Learning and Onboard Spacecraft Diagnostics - Matthew T. Hansen; Jason A. Dittmann (The Astronomical Journal)
Exoplanet discovery at long orbital periods requires reliably detecting individual transits without additional information about the system. Common techniques, like phase folding of light curves and periodogram analysis of radial velocity data, are more sensitive to planets with shorter orbital periods, leaving a dearth of planet discoveries at long periods. We present a novel technique using an ensemble of convolutional neural networks incorporating the onboard spacecraft diagnostics of Kepler to classify transits within a light curve. We create a pipeline to recover the location of individual transits, and the period of the orbiting planet, which possesses an accuracy of >80% in the long orbital period regime (50 ≤ P ≤ 800 days). Our neural network pipeline has the potential to discover additional planets in the Kepler data set, and crucially, within the η -Earth regime. We report our first candidate from this pipeline, KOI-1271.02. KOI-1271.01 is known to exhibit strong transit timing variations (TTVs), and so we jointly model the TTVs and transits of both transiting planets to constrain the orbital configuration and planetary parameters and conclude with a series of potential parameters for KOI-1271.02, as there are not enough data currently to uniquely constrain the system. We conclude that KOI-1271.02 has a radius of 5.32 ± 0.20 R _⊕ and most likely a mass of ${28.94}_{-0.47}^{0.23}$ M _⊕ . Future constraints on the nature of KOI-1271.02 require measuring additional TTVs of KOI-1271.01 or observing a second transit of KOI-1271.02.