Advances in the Kepler Transit Search Engine

Abstract

Twenty years ago, no planets were known outside our own solar system. Since then, the discoveries of ~1500 exoplanets have radically altered our views of planets and planetary systems. This revolution is due in no small part to the Kepler Mission, which has discovered >1000 of these planets and >4000 planet candidates. While Kepler has shown that small rocky planets and planetary systems are quite common, the quest to find Earth's closest cousins and characterize their atmospheres presses forward with missions such as NASA Explorer Program's Transiting Exoplanet Survey Satellite (TESS) slated for launch in 2017 and ESA's PLATO mission scheduled for launch in 2024.

These future missions pose daunting data processing challenges in terms of the number of stars, the amount of data, and the difficulties in detecting weak signatures of transiting small planets against a roaring background. These complications include instrument noise and systematic effects as well as the intrinsic stellar variability of the subjects under scrutiny. In this paper we review recent developments in the Kepler transit search pipeline improving both the yield and reliability of detected transit signatures.

Many of the phenomena in light curves that represent noise can also trigger transit detection algorithms. The Kepler Mission has expended great effort in suppressing false positives from its planetary candidate catalogs. Over 18,000 transit-like signatures can be identified for a search across 4 years of data. Most of these signatures are artifacts, not planets. Vetting all such signatures historically takes several months' effort by many individuals. We describe the application of machine learning approaches for the automated vetting and production of planet candidate catalogs. These algorithms can improve the efficiency of the human vetting effort as well as quantifying the likelihood that each candidate is truly a planet. This information is crucial for obtaining valid planet occurrence rates. Machine learning approaches may prove to be critical to the success of future missions such as TESS and PLATO.