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.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
other 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Discovery and spectro-photodynamical characterization of TIC 295741342, a coplanar triply-eclipsing triple with a giant tertiary showing two degenerate evolutionary states and predicted Roche lobe overflow.
Four solar-type twin binaries show evolutionary diversity from main-sequence to red-giant stages with varying magnetic activity, including possible triple-system signatures in one case.
citing papers explorer
-
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.
-
TIC 295741342: A Triply-Eclipsing Triple Star System with a Giant Tertiary
Discovery and spectro-photodynamical characterization of TIC 295741342, a coplanar triply-eclipsing triple with a giant tertiary showing two degenerate evolutionary states and predicted Roche lobe overflow.
-
Diversity in Evolutionary Status and Magnetic Activity among Solar-Type Twin Detached Eclipsing Binaries
Four solar-type twin binaries show evolutionary diversity from main-sequence to red-giant stages with varying magnetic activity, including possible triple-system signatures in one case.