DART-Vetter: A Deep LeARning Tool For Automatic Triage Of Exoplanet Candidates

In the identification of new planetary candidates in transit surveys, the employment of Deep Learning models proved to be essential to efficiently analyse a continuously growing volume of photometric observations.

To further improve the robustness of these models, it is necessary to exploit the complementarity of data collected from different transit surveys such as NASA’s Kepler, Transiting Exoplanet Survey Satellite (TESS), and, in the near future, the ESA PLAnetary Transits and Oscillation of stars (PLATO) mission.

In this work, we present a Deep Learning model, named DART-Vetter, able to distinguish planetary candidates (PC) from false positives signals (NPC) detected by any potential transiting survey. DART-Vetter is a Convolutional Neural Network that processes only the light curves folded on the period of the relative signal, featuring a simpler and more compact architecture with respect to other triaging and/or vetting models available in the literature.

We trained and tested DART-Vetter on several dataset of publicly available and homogeneously labelled TESS and Kepler light curves in order to prove the effectiveness of our model. Despite its simplicity, DART-Vetter achieves highly competitive triaging performance, with a recall rate of 91% on an ensemble of TESS and Kepler data, when compared to Exominer and Astronet-Triage.

Its compact, open source and easy to replicate architecture makes DART-Vetter a particularly useful tool for automatizing triaging procedures or assisting human vetters, showing a discrete generalization on TCEs with Multiple Event Statistic (MES) > 20 and orbital period < 50 days.

Stefano Fiscale (1 and 2 and 3), Laura Inno (2 and 3), Alessandra Rotundi (1 and 2), Angelo Ciaramella (2), Alessio Ferone (2), Christian Magliano (3 and 4), Luca Cacciapuoti (5), Veselin Kostov (6 and 7), Elisa Quintana (6), Giovanni Covone (3 and 4 and 8), Maria Teresa Muscari Tomajoli (1 and 2), Vito Saggese (4), Luca Tonietti (1 and 2 and 3 and 9), Antonio Vanzanella (10), Vincenzo Della Corte (3) ((1) UNESCO Chair “Environment, Resources and Sustainable Development”, Department of Science and Technology, Parthenope University of Naples, Italy, (2) Department of Science and Technology, Parthenope University of Naples, Centro Direzionale di Napoli, Naples, I-80143, Italy, (3) INAF, Osservatorio Astronomico di Capodimonte, Salita Moiariello, 16, Naples, I-80131, Italy, (4) Department of Physics “Ettore Pancini”, University of Naples Federico II, Naples, Italy, (5) European Southern Observatory, Karl-Schwarzschild-Strasse 2 D-85748 Garching bei Munchen, Germany, (6) NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, USA, (7) Citizen Scientist, Planet Patrol Collaboration, Greenbelt, MD, 20771, USA, (8) INFN section of Naples, Via Cinthia 6, 80126, Napoli, Italy, (9) Department of Biology, Federico II University of Naples, Naples, Italy, (10) National centre for Nuclear Research, Pasteura 7, 02-093, Warsaw, Poland)

Comments: Number of pages: 24, Number of figures: 8, Article accepted for publication in The Astronomical Journal on 2025-05-30
Subjects: Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG)
Cite as: arXiv:2506.05556 [astro-ph.EP] (or arXiv:2506.05556v1 [astro-ph.EP] for this version)
https://doi.org/10.48550/arXiv.2506.05556
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Related DOI:
https://doi.org/10.3847/1538-3881/addf4d
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Submission history
From: Stefano Fiscale
[v1] Thu, 5 Jun 2025 20:05:16 UTC (1,511 KB)
https://arxiv.org/abs/2506.05556
Astrobiology,