ExoMiner++ 2.0: Vetting TESS Full-Frame Image Transit Signals
This work incrementally extends an existing method to a broader dataset, aiding astronomers in exoplanet discovery and follow-up prioritization.
The researchers tackled the challenge of identifying exoplanet transit signals in TESS Full-Frame Images, which are more difficult to analyze than standard data, and found that their adapted ExoMiner++ 2.0 model effectively generalized to this domain, providing robust discrimination between planetary signals and false positives.
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.