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

2026-01-21

AI Summary

This study addresses the challenge of distinguishing genuine transiting exoplanet signals from astrophysical false positives and instrumental artifacts in TESS Full Frame Images (FFIs), which suffer from long cadence sampling. For the first time, the deep learning framework ExoMiner++ is successfully generalized to TESS FFI data through the development of ExoMiner++ 2.0. This model integrates light curve feature engineering with a multi-condition validation strategy to perform large-scale planetary versus non-planetary classification of threshold-crossing events across all sectors. The approach demonstrates robust performance under diverse observational conditions, significantly enhancing the reliability of transit signal identification in long-cadence data. It produces a uniform catalog of validated signals, thereby extending the applicability of ExoMiner++ to the full TESS dataset and providing critical support for exoplanet population statistics and prioritization of follow-up observations.

Technology Category

• Machine Learning: Transparent, Interpretable, Explainable ML
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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.

Problem

• TESS
• Full-Frame Images
• transit signals
• exoplanet vetting
• false positives

Innovation

• ExoMiner++ 2.0
• TESS Full-Frame Images
• transit vetting
• machine learning classification
• exoplanet detection