ExoMiner++ 2.0: Vetting TESS Full-Frame Image Transit Signals

TL;DR

This paper adapts the ExoMiner++ framework to TESS Full-Frame Image data, enabling large-scale classification of transit signals and improving vetting accuracy across the full dataset.

Contribution

It extends ExoMiner++ to FFI data, demonstrating effective generalization and robust classification of planetary signals and false positives.

Findings

ExoMiner++ 2.0 performs well on FFI data despite longer cadence limitations.

The model effectively discriminates between planets, false positives, and artifacts.

The approach supports large-scale TESS data analysis and follow-up prioritization.

Abstract

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.