A W-Net deep learning model detects asteroids in TESS data independently of trajectory by rotating training image cubes and using adaptive normalization for data scaling.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Seyfert 2 galaxies show detectable optical variability, enabling estimates of scattering region sizes consistent with the obscuring torus via amplitude matching to Seyfert 1s.
citing papers explorer
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Trajectory-Agnostic Asteroid Detection in TESS with Deep Learning
A W-Net deep learning model detects asteroids in TESS data independently of trajectory by rotating training image cubes and using adaptive normalization for data scaling.
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Detection of Variability in Seyfert 2 Galaxies and Measurement of the Optical Scattering Region Size
Seyfert 2 galaxies show detectable optical variability, enabling estimates of scattering region sizes consistent with the obscuring torus via amplitude matching to Seyfert 1s.