A new chi-square morphology method plus CNN classifies Kepler eclipsing binaries at 90% accuracy and flags four new temporally varying systems linked to magnetic activity.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
astro-ph.SR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A neural network classifies 20,196 TESS eclipsing binaries into 13,376 EA, 2,114 EB, and 4,706 EW systems after achieving 99% accuracy on held-out test data.
citing papers explorer
-
A New Methodology for Classifying Eclipsing Binaries with Kepler Data and Deep Learning
A new chi-square morphology method plus CNN classifies Kepler eclipsing binaries at 90% accuracy and flags four new temporally varying systems linked to magnetic activity.
-
The Phenomenological Classification of TESS Eclipsing Binaries
A neural network classifies 20,196 TESS eclipsing binaries into 13,376 EA, 2,114 EB, and 4,706 EW systems after achieving 99% accuracy on held-out test data.