Automatic search for transiting planets in TESS-SPOC FFIs with RAVEN: over 100 newly validated planets and over 2000 vetted candidates
M. Lafarga, D. J. Armstrong, K. Cui, A. Hadjigeorghiou, V. Kunovac, L. Doyle, E. M. Bryant, R. F. D\'iaz, L. A. Nieto, A. Osborn

TL;DR
This paper introduces RAVEN, a new pipeline that detects and validates transiting planets in TESS data, resulting in over 100 newly confirmed planets and 2000 high-probability candidates, enhancing the TESS planet catalog.
Contribution
The study presents a uniform search and validation method using RAVEN, improving the completeness and reliability of TESS transiting planet candidate samples.
Findings
Validated 118 planets, including 31 new detections.
Identified over 2000 high-probability planet candidates.
Detected a significant number of large-radius and mono/duo-transiting candidates.
Abstract
Space-based missions such as TESS are identifying a wealth of short-period ( d) transiting planets. Despite the growing number of confirmed and candidate planets, the sample is still incomplete and highly biased, challenging demographic studies. Moreover, there are still a large number of unconfirmed candidates that can end up being false positives. We use the new pipeline RAVEN to perform a uniform search and validation of transiting planet candidates in TESS data. We focus on a magnitude-limited sample of over 2.2 million main sequence stars well characterised by Gaia and observed by TESS in its Full Frame Images during its first 4 years of operations (sectors 1 to 55). We aim to detect candidates with periods within days. RAVEN detects candidates with a box least squares algorithm, classifies them into transiting planets and false positives using machine learning…
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