ExoMiner++: Enhanced Transit Classification and a New Vetting Catalog for 2-Minute TESS Data
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| Format: | Preprint |
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2025
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| author | Valizadegan, Hamed Martinho, Miguel J. S. Jenkins, Jon M. Twicken, Joseph D. Caldwell, Douglas A. Maynard, Patrick Wei, Hongbo Zhong, William Yates, Charles Donald, Sam Collins, Karen A. Latham, David Barkaoui, Khalid Calkins, Michael L. Carden, Kylee Chazov, Nikita Esquerdo, Gilbert A. Guillot, Tristan Krushinsky, Vadim Nowak, Grzegorz Rackham, Benjamin V. Triaud, Amaury Schwarz, Richard P. Stephens, Denise Stockdale, Chris Watkins, Cristilyn N. Wilkin, Francis P. |
| author_facet | Valizadegan, Hamed Martinho, Miguel J. S. Jenkins, Jon M. Twicken, Joseph D. Caldwell, Douglas A. Maynard, Patrick Wei, Hongbo Zhong, William Yates, Charles Donald, Sam Collins, Karen A. Latham, David Barkaoui, Khalid Calkins, Michael L. Carden, Kylee Chazov, Nikita Esquerdo, Gilbert A. Guillot, Tristan Krushinsky, Vadim Nowak, Grzegorz Rackham, Benjamin V. Triaud, Amaury Schwarz, Richard P. Stephens, Denise Stockdale, Chris Watkins, Cristilyn N. Wilkin, Francis P. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09790 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ExoMiner++: Enhanced Transit Classification and a New Vetting Catalog for 2-Minute TESS Data Valizadegan, Hamed Martinho, Miguel J. S. Jenkins, Jon M. Twicken, Joseph D. Caldwell, Douglas A. Maynard, Patrick Wei, Hongbo Zhong, William Yates, Charles Donald, Sam Collins, Karen A. Latham, David Barkaoui, Khalid Calkins, Michael L. Carden, Kylee Chazov, Nikita Esquerdo, Gilbert A. Guillot, Tristan Krushinsky, Vadim Nowak, Grzegorz Rackham, Benjamin V. Triaud, Amaury Schwarz, Richard P. Stephens, Denise Stockdale, Chris Watkins, Cristilyn N. Wilkin, Francis P. Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics Machine Learning 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. |
| title | ExoMiner++: Enhanced Transit Classification and a New Vetting Catalog for 2-Minute TESS Data |
| topic | Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2502.09790 |