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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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3representative citing papers
SCAT DR1 delivers 1810 spectra of 1330 transients with classifications, fitted light curves, new redshifts for many host galaxies, and host properties as a testbed for photometric classification pipelines.
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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SCAT Data Release 1: 1810 optical spectra of 1330 transients
SCAT DR1 delivers 1810 spectra of 1330 transients with classifications, fitted light curves, new redshifts for many host galaxies, and host properties as a testbed for photometric classification pipelines.
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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.