An ensemble ML framework achieves 90.7% morphology classification accuracy and R² values of 0.77–0.92 for key parameters on held-out test data, with external validation against OGLE and Kepler catalogs.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 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.
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Is the `Known' Enough? An Integrated Machine Learning Framework for Eclipsing Binary Classification and Parameter Estimation Based on Well-Characterized Systems
An ensemble ML framework achieves 90.7% morphology classification accuracy and R² values of 0.77–0.92 for key parameters on held-out test data, with external validation against OGLE and Kepler catalogs.
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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.