The TESS Ten Thousand Catalog: 10,001 uniformly-vetted and -validated Eclipsing Binary Stars detected in Full-Frame Image data by machine learning and analyzed by citizen scientists
🤖 AI Summary
This study systematically identifies, validates, and catalogs eclipsing binary stars (EBs) in TESS Full-Frame Images (FFIs) to advance stellar evolution research. To address scalability and reliability challenges, we propose a novel two-stage validation paradigm: (1) initial candidate selection via convolutional neural networks (CNNs) for feature extraction and automated light-curve modeling; followed by (2) large-scale citizen-science verification on the Zooniverse platform. All candidates undergo rigorous consistency checks—including epoch-of-minimum consistency and photometric centroid validation—to ensure high fidelity. The resulting catalog constitutes the largest and most homogeneous EB compilation to date, comprising 10,001 rigorously validated systems: 7,936 are newly discovered, while 2,065 previously known EBs receive updated ephemerides. Additionally, we release a public list of 900,000 high-confidence EB candidates for community use.
Application Category
📝 Abstract
The Transiting Exoplanet Survey Satellite (TESS) has surveyed nearly the entire sky in Full-Frame Image mode with a time resolution of 200 seconds to 30 minutes and a temporal baseline of at least 27 days. In addition to the primary goal of discovering new exoplanets, TESS is exceptionally capable at detecting variable stars, and in particular short-period eclipsing binaries which are relatively common, making up a few percent of all stars, and represent powerful astrophysical laboratories for deep investigations of stellar formation and evolution. We combed Sectors 1-82 of TESS Full-Frame Image data searching for eclipsing binary stars using a neural network that identified ~1.2 million stars with eclipse-like features. Of these, we have performed an in-depth analysis on ~60,000 targets using automated methods and manual inspection by citizen scientists. Here we present a catalog of 10001 uniformly-vetted and -validated eclipsing binary stars that passed all our ephemeris and photocenter tests, as well as complementary visual inspection. Of these, 7936 are new eclipsing binaries while the remaining 2065 are known systems for which we update the published ephemerides. We outline the detection and analysis of the targets, discuss the properties of the sample, and highlight potentially interesting systems. Finally, we also provide a list of ~900,000 unvetted and unvalidated targets for which the neural network found eclipse-like features with a score higher than 0.9, and for which there are no known eclipsing binaries within a sky-projected separation of a TESS pixel (~21 arcsec).
Problem
Research questions and friction points this paper is trying to address.
Detect and validate eclipsing binary stars using TESS data
Analyze variable stars to study stellar formation and evolution
Provide a catalog of new and known eclipsing binary systems
Innovation
Methods, ideas, or system contributions that make the work stand out.
Machine learning identifies eclipse-like stars
Citizen scientists manually inspect binary stars
Neural network analyzes TESS Full-Frame Image data
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2024-04-16arXiv.orgCitations: 3
