The Detection of Transiting Exoplanets by Gaia
Aviad Panahi, Shay Zucker, Gisella Clementini, Marc Audard, Avraham, Binnenfeld, Felice Cusano, Dafydd Wyn Evans, Roy Gomel, Berry Holl, Ilya, Ilyin, Gr\'egory Jevardat de Fombelle, Tsevi Mazeh, Nami Mowlavi, Krzysztof, Nienartowicz, Lorenzo Rimoldini, Sahar Shahaf

TL;DR
This paper presents a novel method for detecting transiting exoplanets using Gaia's photometric data, successfully identifying two hot Jupiters, Gaia-1b and Gaia-2b, confirming Gaia's potential in exoplanet discovery.
Contribution
The paper introduces a combined approach using machine learning and the Box-Least-Square method to detect exoplanets in Gaia data, validated by TESS and radial-velocity follow-up.
Findings
First exoplanets detected by Gaia: Gaia-1b and Gaia-2b.
Effective detection method combining machine learning and BLS.
Validation through TESS imaging and LBT radial velocities.
Abstract
Context: The space telescope Gaia is dedicated mainly to performing high-precision astrometry, but also spectroscopy and epoch photometry which can be used to study various types of photometric variability. One such variability type is exoplanetary transits. The photometric data accumulated so far have finally matured enough to allow the detection of some exoplanets. Aims: In order to fully exploit the scientific potential of Gaia, we search its photometric data for the signatures of exoplanetary transits. Methods: The search relies on a version of the Box-Least-Square (BLS) method, applied to a set of stars prioritized by machine-learning classification methods. An independent photometric validation was obtained using the public full-frame images of TESS. In order to validate the first two candidates, radial-velocity follow-up observations were performed using the spectrograph…
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Taxonomy
TopicsAstronomy and Astrophysical Research · Stellar, planetary, and galactic studies · Astronomical Observations and Instrumentation
