ExoMiner++: Enhanced Transit Classification and a New Vetting Catalog for 2-Minute TESS Data
Journal:
arXiv
Published Date:
Feb 13, 2025
Abstract
We present ExoMiner++, an enhanced deep learning model that builds on the
success of ExoMiner to improve transit signal classification in 2-minute TESS
data. ExoMiner++ incorporates additional diagnostic inputs, including
periodogram, flux trend, difference image, unfolded flux, and spacecraft
attitude control data, all of which are crucial for effectively distinguishing
transit signals from more challenging sources of false positives. To further
enhance performance, we leverage multi-source training by combining
high-quality labeled data from the Kepler space telescope with TESS data. This
approach mitigates the impact of TESS's noisier and more ambiguous labels.
ExoMiner++ achieves high accuracy across various classification and ranking
metrics, significantly narrowing the search space for follow-up investigations
to confirm new planets. To serve the exoplanet community, we introduce new TESS
catalog containing ExoMiner++ classifications and confidence scores for each
transit signal. Among the 147,568 unlabeled TCEs, ExoMiner++ identifies 7,330
as planet candidates, with the remainder classified as false positives. These
7,330 planet candidates correspond to 1,868 existing TESS Objects of Interest
(TOIs), 69 Community TESS Objects of Interest (CTOIs), and 50 newly introduced
CTOIs. 1,797 out of the 2,506 TOIs previously labeled as planet candidates in
ExoFOP are classified as planet candidates by ExoMiner++. This reduction in
plausible candidates combined with the excellent ranking quality of ExoMiner++
allows the follow-up efforts to be focused on the most likely candidates,
increasing the overall planet yield.
