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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ASTRAFier is a Transformer-BiLSTM-CNN model that classifies stellar variability from light curves, reporting 94.26% accuracy on Kepler data and 88.22% on TESS, then applied to 2.8 million TESS curves to release a catalog.
A quantile-graph PCA SOM embedding creates a map of 1.5 million TESS light curves where proximity reflects similarity in variability amplitude, timescale, SNR, and shape, with stable positions for repeat observations.
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
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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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ASTRAFier: A Novel and Scalable Transformer-based Stellar Variability Classifier
ASTRAFier is a Transformer-BiLSTM-CNN model that classifies stellar variability from light curves, reporting 94.26% accuracy on Kepler data and 88.22% on TESS, then applied to 2.8 million TESS curves to release a catalog.
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A useful representation of TESS light curves
A quantile-graph PCA SOM embedding creates a map of 1.5 million TESS light curves where proximity reflects similarity in variability amplitude, timescale, SNR, and shape, with stable positions for repeat observations.
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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.