SearcharxivSearch

arXiv subjects

Matthew T. Hansen

Publications and source records attributed to Matthew T. Hansen.

2 recordsLinked to original sources

Exploring Long-period Architectures: Four New Planet Candidates from Kepler with Periods >342 days

The Kepler detection pipeline, as well as the transit method, has a bias towards shorter periods, leaving a dearth of detections at longer orbital periods. This relative lack of detections has left an incomplete picture of the architectures of exoplanet systems within the long-period regime. We have built a single transit detection pipeline, utilizing a classification convolutional neural network and the onboard spacecraft diagnostics of the Kepler spacecraft, to detect long-period planets. We apply our pipeline to all currently known planetary systems in the Kepler field hosting at least one planet with an orbital period longer than 6 days. We manually vet all new signals from our pipeline, and identify four new planetary candidates, all of which are in systems where the inner planets exhibit transit timing variations (TTVs). Two of these candidates, Kepler 1752.02 and Kepler 199.03, cause two transit events that are consistent with periods of $777.78^{+0.01}_{-0.02}$ and $505.495^{+0.004}_{-0.004}$ days, and radii of $3.55^{+0.15}_{-0.15}$ and $2.74^{+0.05}_{-0.05}$ $R_{\oplus}$, respectively. Our remaining two candidates, Kepler 1897.02 and Kepler 1811.02, are single transit candidates with radii $4.81^{+0.20}_{-0.19}$ and $3.25^{+0.28}_{-0.30}$ $R_{\oplus}$, respectively. The shortest orbital periods for these candidates, consistent with the Kepler dataset (gaps and coverage), are 342 days for Kepler 1897.02 and 544 days for Kepler 1811.02. The new planetary candidates, on their own, are incapable of reproducing the observed TTV signals in the inner system. Although difficult to schedule, follow-up observations are needed to further constrain the new candidates and potentially discover the planets causing the perturbations.

astro-ph.EP

Single Transit Detection In Kepler With Machine Learning And Onboard Spacecraft Diagnostics

Exoplanet discovery at long orbital periods requires reliably detecting individual transits without additional information about the system. 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 \emph{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 maintains $>80\%$ transit recovery sensitivity out to an 800-day orbital period. Our neural network pipeline has the potential to discover additional planets in the \emph{Kepler} dataset, 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 is not enough data currently to uniquely constrain the system. We conclude that KOI 1271.02 has a radius of 5.32 $\pm$ 0.20 $R_{\oplus}$ and a mass of $28.94^{0.23}_{-0.47}$ $M_{\oplus}$. 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.

astro-ph.EP