StarCLR pretrains on TESS light curves via contrastive learning on overlapping subsequences and improves variable star classification F1 scores over scratch-trained models when fine-tuned on TESS, ZTF, and Gaia.
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3 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
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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.
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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StarCLR: Contrastive Learning Representation for Astronomical Light Curves
StarCLR pretrains on TESS light curves via contrastive learning on overlapping subsequences and improves variable star classification F1 scores over scratch-trained models when fine-tuned on TESS, ZTF, and Gaia.
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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.