Abstract:NASA's Transiting Exoplanet Survey Satellite (TESS) has identified thousands of exoplanet candidates, yet many remain unconfirmed due to the limitations of manual vetting processes. This paper presents ExoNet, a multimodal deep learning framework that integrates phase-folded global and local light curve representations with stellar parameters using a late-fusion architecture combining 1D Convolutional Neural Networks and Multi-Head Attention. Trained on labeled Kepler data, ExoNet achieves strong classification performance and demonstrates effective generalization to TESS data. Applied to 200 unconfirmed TESS planet candidates, the model identifies multiple high-confidence candidates, including several within the habitable zone. The results highlight the effectiveness of multimodal fusion and attention mechanisms in automated exoplanet candidate validation.
| Comments: | 8 pages, 4 figures, 4 tables |
| Subjects: | Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.15560 [astro-ph.EP] |
| (or arXiv:2604.15560v1 [astro-ph.EP] for this version) | |
| https://doi.org/10.48550/arXiv.2604.15560 arXiv-issued DOI via DataCite (pending registration) |
From: Md Rashadul Islam [view email]
[v1]
Thu, 16 Apr 2026 22:26:29 UTC (415 KB)
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