DART-Vetter: A Deep LeARning Tool for automatic triage of exoplanet candidates
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| Format: | Preprint |
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2025
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| author | Fiscale, Stefano Inno, Laura Rotundi, Alessandra Ciaramella, Angelo Ferone, Alessio Magliano, Christian Cacciapuoti, Luca Kostov, Veselin Quintana, Elisa Covone, Giovanni Tomajoli, Maria Teresa Muscari Saggese, Vito Tonietti, Luca Vanzanella, Antonio Della Corte, Vincenzo |
| author_facet | Fiscale, Stefano Inno, Laura Rotundi, Alessandra Ciaramella, Angelo Ferone, Alessio Magliano, Christian Cacciapuoti, Luca Kostov, Veselin Quintana, Elisa Covone, Giovanni Tomajoli, Maria Teresa Muscari Saggese, Vito Tonietti, Luca Vanzanella, Antonio Della Corte, Vincenzo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05556 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DART-Vetter: A Deep LeARning Tool for automatic triage of exoplanet candidates Fiscale, Stefano Inno, Laura Rotundi, Alessandra Ciaramella, Angelo Ferone, Alessio Magliano, Christian Cacciapuoti, Luca Kostov, Veselin Quintana, Elisa Covone, Giovanni Tomajoli, Maria Teresa Muscari Saggese, Vito Tonietti, Luca Vanzanella, Antonio Della Corte, Vincenzo Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics Machine Learning 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. |
| title | DART-Vetter: A Deep LeARning Tool for automatic triage of exoplanet candidates |
| topic | Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2506.05556 |