Planetary Candidates Observed by Kepler. VII. The First Fully Uniform Catalog Based on The Entire 48 Month Dataset (Q1-Q17 DR24) (1512.06149v2)

Abstract: We present the seventh Kepler planet candidate catalog, which is the first to be based on the entire, uniformly processed, 48 month Kepler dataset. This is the first fully automated catalog, employing robotic vetting procedures to uniformly evaluate every periodic signal detected by the Q1-Q17 Data Release 24 (DR24) Kepler pipeline. While we prioritize uniform vetting over the absolute correctness of individual objects, we find that our robotic vetting is overall comparable to, and in most cases is superior to, the human vetting procedures employed by past catalogs. This catalog is the first to utilize artificial transit injection to evaluate the performance of our vetting procedures and quantify potential biases, which are essential for accurate computation of planetary occurrence rates. With respect to the cumulative Kepler Object of Interest (KOI) catalog, we designate 1,478 new KOIs, of which 402 are dispositioned as planet candidates (PCs). Also, 237 KOIs dispositioned as false positives (FPs) in previous Kepler catalogs have their disposition changed to PC and 118 PCs have their disposition changed to FP. This brings the total number of known KOIs to 8,826 and PCs to 4,696. We compare the Q1-Q17 DR24 KOI catalog to previous KOI catalogs, as well as ancillary Kepler catalogs, finding good agreement between them. We highlight new PCs that are both potentially rocky and potentially in the habitable zone of their host stars, many of which orbit solar-type stars. This work represents significant progress in accurately determining the fraction of Earth-size planets in the habitable zone of Sun-like stars. The full catalog is publicly available at the NASA Exoplanet Archive.

Overview of the Seventh Kepler Planet Candidate Catalog

This paper presents the seventh Kepler planet candidate catalog, denoting the first fully uniform catalog derived from the complete 48-month dataset (Q1--Q17 DR24). The focus of the paper was to systematically and uniformly vet every periodic signal identified by the Kepler pipeline, utilizing a fully automated robotic vetting technique. This approach is essential for accurately determining planetary occurrence rates, a key objective of the Kepler mission.

The catalog is distinguished by its application of artificial transit injection to comprehensively assess the vetting procedures and identify potential biases. The researchers report that their robotic techniques generally match or surpass prior human vetting methods. A key advancement is the uniform application of criteria that prevent manual biases and enable reproducibility in current and future research.

Strong Numerical Results and Data

The analysis encompasses 198,646 observed targets, translating to a considerable dataset for evaluation. The researchers cataloged a significant number of new Kepler Objects of Interest (KOIs), refining previous entries by altering the disposition of several candidates from False Positive (FP) to Planet Candidate (PC) and vice versa. This record comprises a total of 4,696 planet candidates and involves a meticulous federation of new candidate signals with pre-established KOIs.

A salient feature of the paper is the deployment of the "Marshall metric" and "Local Preserving Projections" for effectively identifying transit-like signals, which detected a high degree of variance in signal noise primarily at longer periods. Here's a concise summary of key aspects:

• TCE Review: Out of the 18,406 Threshold Crossing Events (TCEs) evaluated, the robotic method demonstrated an effective dispositioning system for classifying candidates.
• Bias Mitigation: With the use of artificial transits, the method measures the detection completeness and robustness of the catalog, ensuring detailed scrutiny that aids in bias mitigation.
• Alignment with Past Catalogs: The new catalog showcases substantial concordance with former KOI catalogs and auxiliary Kepler catalogs, enhancing confidence in the updated classifications.

Implications and Future Work in AI and Astronomy

The uniform application of automation in vetting processes marks significant progress toward creating reliable datasets for the determination of planet occurrence rates. This research suggests the potential for further enhancing exoplanet identification processes using machine learning techniques, extending to AI-driven methodologies in large data analyses, relevant not only for current missions but also for prospective projects like TESS and LSST.

The paper details an expansion of the search to more accurately compute the presence of Earth-size planets in the habitable zone. This work's methodology could potentially revolutionize how planetary signals are analyzed, setting a precedent for minimizing human error in large-scale astronomical data interpretation and extrapolation.

In conclusion, the seventh Kepler catalog acts as a pivotal reference point that future research can build upon, particularly in optimizing automated processes for vetting and classifying cosmic data. The incorporation of artificial transit injection in the analysis process paves the way for more dynamic and detail-aligned exploratory strategies in astronomy. Future developments in AI could utilize patterns and methods highlighted in this research to explore new realms of dataset evaluations across various scientific domains.