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Deep learning W-Net detects asteroids in TESS data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new deep learning method called a W-Net, utilizing two stacked 3D U-Nets, to detect asteroids in TESS image data. This approach is robust to variations in asteroid speed and direction, unlike traditional shift-and-stack algorithms, and includes a novel Adaptive Normalization technique for data scaling. The team has also released the code for generating TESS training data with asteroid masks to aid the scientific community, with potential applications for future missions like the Nancy Grace Roman Space Telescope. AI

IMPACT Enhances astronomical survey capabilities by improving asteroid detection efficiency and robustness.

RANK_REASON Academic paper detailing a new deep learning method for scientific application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep learning W-Net detects asteroids in TESS data

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Academic paper detailing a new deep learning method for scientific application. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jordan Caraballo-Vega ·

    Trajectory-Agnostic Asteroid Detection in TESS with Deep Learning

    We present a novel method for extracting moving objects from TESS data using machine learning. Our approach uses two stacked 3D U-Nets with skip connections, which we call a W-Net, to filter background and identify pixels containing moving objects in TESS image time-series data. …

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