Trajectory-Agnostic Asteroid Detection in TESS with Deep Learning

arXiv:2605.12391 · astro-ph.EP, astro-ph.SR, cs.LG · Submitted 2026-05-12 · Read on arXiv

Brian P. Powell, Jorge Martinez-Palomera, Amy Tuson, Christina Hedges, Jessie Dotson, Jordan Caraballo-Vega

astro-ph.EP, astro-ph.SR, cs.LG

Submitted: 2026-05-12

Comments: Accepted by The Astronomical Journal, 11 May 2026

Code: https://github.com/jorgemarpa/tess-asteroid-ml

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

Terminology

• AUC — 19× in this paper · explained in 2 episodes
• Loss Function — 13× in this paper · explained in 2 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Aperture — 2× in this paper · explained in 1 episode
• Generalizability — 2× in this paper · explained in 6 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Area Under the Curve (AUC) — 1× in this paper · explained in 1 episode
• D Convolutional Neural Network — 1× in this paper
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• False Positive Rate — 1× in this paper
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• International Conference on Learning Representations — 1× in this paper
• Microlensing — 1× in this paper · explained in 2 episodes
• Mikulski Archive for Space Telescopes — 1× in this paper
• NASA Center for Climate Simulation — 1× in this paper
• NASA Jet Propulsion Laboratory — 1× in this paper
• Robustness — 1× in this paper · explained in 5 episodes
• Signal-to-noise ratio (SNR) — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• True Positive Rate — 1× in this paper
• World Coordinate Solution — 1× in this paper

Sources

• Loss Functions in the Era of Semantic Segmentation: A Survey and Outlook
• Layer Normalization
• Neural Machine Translation by Jointly Learning to Align and Translate
• Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
• Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations
• Attention Is All You Need
• W-Net: A Deep Model for Fully Unsupervised Image Segmentation

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