TransitNet: A Compact Attention-Augmented Deep Learning Framework for Low-SNR Transit Blind Searches

arXiv:2606.18932 · astro-ph.EP, astro-ph.IM, cs.AI, cs.LG · Submitted 2026-06-17 · Read on arXiv

Xingchen Yan, Jian Ge, Qingtian Liu, Kevin Willis, Quanquan Hu, Jiapeng Zhu

astro-ph.EP, astro-ph.IM, cs.AI, cs.LG

Submitted: 2026-06-17

Comments: 25 pages, 23 figures, 3 tables, submitted to A&A

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• AUC — 25× in this paper · explained in 2 episodes
• Accuracy — 19× in this paper · explained in 1 episode
• Benchmark — 16× in this paper · explained in 2 episodes
• Generalization — 16× in this paper · explained in 13 episodes
• False Positive — 10× in this paper
• Robustness — 7× in this paper · explained in 5 episodes
• True Positive — 7× in this paper
• Ablation — 6× in this paper · explained in 1 episode
• Ablation Studies — 6× in this paper · explained in 1 episode
• Benchmarking — 5× in this paper · explained in 3 episodes
• Aggregation — 4× in this paper · explained in 1 episode
• Feature Extraction — 4× in this paper · explained in 2 episodes
• Calibration — 3× in this paper · explained in 7 episodes
• Periodic Chunk Permutation — 3× in this paper
• Transformer — 3× in this paper · explained in 2 episodes
• Attention Mechanism — 2× in this paper · explained in 2 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• International Conference on Learning Representations — 2× in this paper
• Interpretability — 2× in this paper · explained in 15 episodes
• Transferability — 2× in this paper · explained in 4 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Aperture — 1× in this paper · explained in 1 episode
• Aperture Photometry — 1× in this paper · explained in 1 episode
• Axioms — 1× in this paper · explained in 2 episodes
• Batch Normalization — 1× in this paper
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Covariate Shift — 1× in this paper · explained in 2 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• False Negative — 1× in this paper
• Gaussian Noise Light Curves — 1× in this paper
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Receiver Operating Characteristic — 1× in this paper
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Signal-to-noise ratio (SNR) — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Transit-Masked Light Curves — 1× in this paper
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• Identifying Exoplanets with Deep Learning: A CNN and RNN Classifier for Kepler DR25 and Candidate Vetting

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