ExoMiner++ 2.0: Vetting TESS Full-Frame Image Transit Signals
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866912862538039296 |
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| author | Martinho, Miguel J. S. Valizadegan, Hamed Jenkins, Jon M. Caldwell, Douglas A. Twicken, Joseph D. Tofflemire, Ben Jafariyazani, Marziye |
| author_facet | Martinho, Miguel J. S. Valizadegan, Hamed Jenkins, Jon M. Caldwell, Douglas A. Twicken, Joseph D. Tofflemire, Ben Jafariyazani, Marziye |
| contents | The Transiting Exoplanet Survey Satellite (TESS) Full-Frame Images (FFIs) provide photometric time series for millions of stars, enabling transit searches beyond the limited set of pre-selected 2-minute targets. However, FFIs present additional challenges for transit identification and vetting. In this work, we apply ExoMiner++ 2.0, an adaptation of the ExoMiner++ framework originally developed for TESS 2-minute data, to FFI light curves. The model is used to perform large-scale planet versus non-planet classification of Threshold Crossing Events across the sectors analyzed in this study. We construct a uniform vetting catalog of all evaluated signals and assess model performance under different observing conditions. We find that ExoMiner++ 2.0 generalizes effectively to the FFI domain, providing robust discrimination between planetary signals, astrophysical false positives, and instrumental artifacts despite the limitations inherent to longer cadence data. This work extends the applicability of ExoMiner++ to the full TESS dataset and supports future population studies and follow-up prioritization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_14877 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ExoMiner++ 2.0: Vetting TESS Full-Frame Image Transit Signals Martinho, Miguel J. S. Valizadegan, Hamed Jenkins, Jon M. Caldwell, Douglas A. Twicken, Joseph D. Tofflemire, Ben Jafariyazani, Marziye Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics Machine Learning The Transiting Exoplanet Survey Satellite (TESS) Full-Frame Images (FFIs) provide photometric time series for millions of stars, enabling transit searches beyond the limited set of pre-selected 2-minute targets. However, FFIs present additional challenges for transit identification and vetting. In this work, we apply ExoMiner++ 2.0, an adaptation of the ExoMiner++ framework originally developed for TESS 2-minute data, to FFI light curves. The model is used to perform large-scale planet versus non-planet classification of Threshold Crossing Events across the sectors analyzed in this study. We construct a uniform vetting catalog of all evaluated signals and assess model performance under different observing conditions. We find that ExoMiner++ 2.0 generalizes effectively to the FFI domain, providing robust discrimination between planetary signals, astrophysical false positives, and instrumental artifacts despite the limitations inherent to longer cadence data. This work extends the applicability of ExoMiner++ to the full TESS dataset and supports future population studies and follow-up prioritization. |
| title | ExoMiner++ 2.0: Vetting TESS Full-Frame Image Transit Signals |
| topic | Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2601.14877 |