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Register a new userKepler Data Validation I -- Architecture, Diagnostic Tests, and Data Products for Vetting Transiting Planet Candidates
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The Kepler Mission was designed to identify and characterize transiting planets in the Kepler Field of View and to determine their occurrence rates. Emphasis was placed on identification of Earth-size planets orbiting in the Habitable Zone of their host stars. Science data were acquired for a period of four years. Long-cadence data with 29.4 min sampling were obtained for ~200,000 individual stellar targets in at least one observing quarter in the primary Kepler Mission. Light curves for target stars are extracted in the Kepler Science Data Processing Pipeline, and are searched for transiting planet signatures. A Threshold Crossing Event is generated in the transit search for targets where the transit detection threshold is exceeded and transit consistency checks are satisfied. These targets are subjected to further scrutiny in the Data Validation (DV) component of the Pipeline. Transiting planet candidates are characterized in DV, and light curves are searched for additional planets after transit signatures are modeled and removed. A suite of diagnostic tests is performed on all candidates to aid in discrimination between genuine transiting planets and instrumental or astrophysical false positives. Data products are generated per target and planet candidate to document and display transiting planet model fit and diagnostic test results. These products are exported to the Exoplanet Archive at the NASA Exoplanet Science Institute, and are available to the community. We describe the DV architecture and diagnostic tests, and provide a brief overview of the data products. Transiting planet modeling and the search for multiple planets on individual targets are described in a companion paper. The final revision of the Kepler Pipeline code base is available to the general public through GitHub. The Kepler Pipeline has also been modified to support the TESS Mission which will commence in 2018.
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We present TRICERATOPS, a new Bayesian tool that can be used to vet and validate TESS Objects of Interest (TOIs). We test the tool on 68 TOIs that have been previously confirmed as planets or rejected as astrophysical false positives. By looking in the false positive probability (FPP) -- nearby false positive probability (NFPP) plane, we define criteria that TOIs must meet to be classified as validated planets (FPP < 0.015 and NFPP < 10^-3), likely planets (FPP < 0.5 and NFPP < 10^-3), and likely nearby false positives (NFPP > 10^-1). We apply this procedure on 384 unclassified TOIs and statistically validate 12, classify 125 as likely planets, and classify 52 as likely nearby false positives. Of the 12 statistically validated planets, 9 are newly validated. TRICERATOPS is currently the only TESS vetting and validation tool that models transits from nearby contaminant stars in addition to the target star. We therefore encourage use of this tool to prioritize follow-up observations that confirm bona fide planets and identify false positives originating from nearby stars.
A main goal of NASAs Kepler Mission is to establish the frequency of potentially habitable Earth-size planets (eta Earth). Relatively few such candidates identified by the mission can be confirmed to be rocky via dynamical measurement of their mass. Here we report an effort to validate 18 of them statistically using the BLENDER technique, by showing that the likelihood they are true planets is far greater than that of a false positive. Our analysis incorporates follow-up observations including high-resolution optical and near-infrared spectroscopy, high-resolution imaging, and information from the analysis of the flux centroids of the Kepler observations themselves. While many of these candidates have been previously validated by others, the confidence levels reported typically ignore the possibility that the planet may transit a different star than the target along the same line of sight. If that were the case, a planet that appears small enough to be rocky may actually be considerably larger and therefore less interesting from the point of view of habitability. We take this into consideration here, and are able to validate 15 of our candidates at a 99.73% (3 sigma) significance level or higher, and the other three at slightly lower confidence. We characterize the GKM host stars using available ground-based observations and provide updated parameters for the planets, with sizes between 0.8 and 2.9 Earth radii. Seven of them (KOI-0438.02, 0463.01, 2418.01, 2626.01, 3282.01, 4036.01, and 5856.01) have a better than 50% chance of being smaller than 2 Earth radii and being in the habitable zone of their host stars.
We report the latest Planet Hunter results, including PH2 b, a Jupiter-size (R_PL = 10.12 pm 0.56 R_E) planet orbiting in the habitable zone of a solar-type star. PH2 b was elevated from candidate status when a series of false positive tests yielded a 99.9% confidence level that transit events detected around the star KIC 12735740 had a planetary origin. Planet Hunter volunteers have also discovered 42 new planet candidates in the Kepler public archive data, of which 33 have at least three transits recorded. Most of these transit candidates have orbital periods longer than 100 days and 20 are potentially located in the habitable zones of their host stars. Nine candidates were detected with only two transit events and the prospective periods are longer than 400 days. The photometric models suggest that these objects have radii that range between Neptune to Jupiter. These detections nearly double the number of gas giant planet candidates orbiting at habitable zone distances. We conducted spectroscopic observations for nine of the brighter targets to improve the stellar parameters and we obtained adaptive optics imaging for four of the stars to search for blended background or foreground stars that could confuse our photometric modeling. We present an iterative analysis method to derive the stellar and planet properties and uncertainties by combining the available spectroscopic parameters, stellar evolution models, and transiting light curve parameters, weighted by the measurement errors. Planet Hunters is a citizen science project that crowd-sources the assessment of NASA Kepler light curves. The discovery of these 43 planet candidates demonstrates the success of citizen scientists at identifying planet candidates, even in longer period orbits with only two or three transit events.
State of the art exoplanet transit surveys are producing ever increasing quantities of data. To make the best use of this resource, in detecting interesting planetary systems or in determining accurate planetary population statistics, requires new automated methods. Here we describe a machine learning algorithm that forms an integral part of the pipeline for the NGTS transit survey, demonstrating the efficacy of machine learning in selecting planetary candidates from multi-night ground based survey data. Our method uses a combination of random forests and self-organising-maps to rank planetary candidates, achieving an AUC score of 97.6% in ranking 12368 injected planets against 27496 false positives in the NGTS data. We build on past examples by using injected transit signals to form a training set, a necessary development for applying similar methods to upcoming surveys. We also make the texttt{autovet} code used to implement the algorithm publicly accessible. texttt{autovet} is designed to perform machine learned vetting of planetary candidates, and can utilise a variety of methods. The apparent robustness of machine learning techniques, whether on space-based or the qualitatively different ground-based data, highlights their importance to future surveys such as TESS and PLATO and the need to better understand their advantages and pitfalls in an exoplanetary context.
We visually inspected the light curves of 7557 Kepler Objects of Interest (KOIs) to search for single transit events (STEs) possibly due to long-period giant planets. We identified 28 STEs in 24 KOIs, among which 14 events are newly reported in this paper. We estimate the radius and orbital period of the objects causing STEs by fitting the STE light curves simultaneously with the transits of the other planets in the system or with the prior information on the host star density. As a result, we found that STEs in seven of those systems are consistent with Neptune- to Jupiter-sized objects of orbital periods ranging from a few to $sim$ $20,mathrm{yr}$. We also estimate that $gtrsim20%$ of the compact multi-transiting systems host cool giant planets with periods $gtrsim 3,mathrm{yr}$ on the basis of their occurrence in the KOIs with multiple candidates, assuming the small mutual inclination between inner and outer planetary orbits.
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