ML based techniques to infer stellar parameters
Papers of interest:
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A Machine Learning Method to Infer Fundamental Stellar Parameters from Photometric Light Curves https://arxiv.org/pdf/1411.1073.pdf
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INFERENCE OF STELLAR PARAMETERS FROM BRIGHTNESS VARIATIONS https://arxiv.org/pdf/1805.04519.pdf
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Preparing for advanced LIGO: A Star-Galaxy Separation Catalog for the Palomar Transient Factory https://arxiv.org/pdf/1703.07356.pdf
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Deep learning Approach for Classifying, Detecting and Predicting Photometric Redshifts of Quasars in the Sloan Digital Sky Survey Stripe 82 https://arxiv.org/pdf/1712.02777.pdf
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A recurrent neural network for classification of unevenly sampled variable stars https://www.nature.com/articles/s41550-017-0321-z#ref-CR11
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IMPROVING GAIA PARALLAX PRECISION WITH A DATA-DRIVEN MODEL OF STARS https://arxiv.org/pdf/1706.05055.pdf