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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2 Pith papers cite this work, alongside 72 external citations. Polarity classification is still indexing.
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Pith papers citing it
72
external citations · OpenAlex
years
2026 2verdicts
UNVERDICTED 2representative citing papers
ZTF high-cadence data shows RR Lyrae stars and flaring sources can mimic UV transients, with pre-existing ML catalogs offering a concrete mitigation approach.
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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The ZTF-ULTRASAT experiment: Characterizing the non-transients in ULTRASAT's high cadence survey
ZTF high-cadence data shows RR Lyrae stars and flaring sources can mimic UV transients, with pre-existing ML catalogs offering a concrete mitigation approach.