Kepler Data Validation I -- Architecture, Diagnostic Tests, and Data Products for Vetting Transiting Planet Candidates

Abstract: 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.

An Analytical Overview of Kepler Data Validation Procedures for Transiting Planet Candidate Vetting

The paper titled "Kepler Data Validation I -- Architecture, Diagnostic Tests, and Data Products for Vetting Transiting Planet Candidates" explores the intricate architecture and methodologies adhered to for validating transiting planet candidates within the Kepler Science Data Processing Pipeline. At its core, the research focuses on the processes employed in the Data Validation (DV) component of the pipeline and the suite of diagnostic tests that aid in overcoming identification challenges to validate the integrity of transit signals captured by the Kepler spacecraft.

Kepler Pipeline and Data Validation

The Kepler Science Data Processing Pipeline is bifurcated into two main components: front-end and back-end operations. The DV component, detailing a critical back-end operation, performs comprehensive vetting on Threshold Crossing Events (TCEs) identified by the Transiting Planet Search (TPS) component. Within the data validation sphere, DV aims to characterize these planet candidates and conduct extensive diagnostic tests to ensure authenticity, thereby assisting scientists in robustly sieving true planets from possible false positives resulting from instrumental artifacts or background celestial objects.

Diagnostic Tests: Precision Tools for Validation

The DV framework incorporates a variety of diagnostic tests specifically designed to discern true planetary transits from false positives. Key elements of this campaign include:

• Weak Secondary Test: This test identifies the presence of secondary eclipse signals within the TCE data. A robust analysis of multiple events across diverse statistics aids in distinguishing between genuine transiting exoplanets and eclipsing binary systems.

• Rolling Band Contamination Diagnostic: Rolling band artifacts, resultant from spacecraft electronics, may mimic transit signatures. This diagnostic flags TCEs coinciding with such artifacts, examining transits against rolling band severity levels.

• Eclipsing Binary Discrimination Tests: DV employs a suite of statistical tests to verify the orbital characteristics consistent with planetary transits, examining odd-even depth variations and period correlations to discriminate between transiting planets and stellar binaries.

• Difference Imaging and Centroid Offset Analysis: By leveraging pixel-level data, this tool locates the spatial source of transit signals, determining whether the source aligns with the intended target, thereby filtering potential background sources from the dataset.

• Centroid Motion Test: This diagnostic seeks out variations in flux-weighted centroid positions during transits, with significant motion indicative of background contamination rather than on-target transits.

• Optical Ghost Diagnostic Test: Newly introduced, this diagnostic investigates correlations between core and halo aperture flux to identify contaminations from optical reflections not associated with the target star.

Implications and Future Prospects

The meticulous design of the Data Validation process is pivotal in ensuring the reliability of planet occurrence statistics derived from Kepler data. The high degree of automation provided by the DV framework enriches the efficiency of transiting exoplanet discovery, enabling detailed investigation through comprehensive data products delivered to the Exoplanet Archive. As the final revision of the pipeline, this methodology also sets a foundation for subsequent missions—such as the Transiting Exoplanet Survey Satellite (TESS)—providing a significantly bolstered pipeline to process voluminous astronomical data with precision.

As observational campaigns expand, employing increasingly sophisticated diagnostic architectures will be essential to parse through more challenging and intricate datasets in planetary science, refining both the identification and characterization of transiting exoplanets. The insights garnered from Kepler's comprehensive approach underscore the imperative for continued evolution in methodologies to confront the diverse array of anomalies present in space-borne astrophysical data collection.