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The Physical Properties of Red Supergiants

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arxiv 0911.4720 v1 pith:7O7EYGVV submitted 2009-11-24 astro-ph.SR

classification astro-ph.SR
keywords propertiesrsgsstarsphysicalstellarcoolextrememany
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Red supergiants (RSGs) are an evolved He-burning phase in the lifetimes of moderately high mass (10 - 25 solar mass) stars. The physical properties of these stars mark them as an important and extreme stage of massive stellar evolution, but determining these properties has been a struggle for many years. The cool extended atmospheres of RSGs place them in an extreme position on the Hertzsprung-Russell diagram and present a significant challenge to the conventional assumptions of stellar atmosphere models. The dusty circumstellar environments of these stars can potentially complicate the determination of their physical properties, and unusual RSGs in the Milky Way and neighboring galaxies present a suite of enigmatic properties and behaviors that strain, and sometimes even defy, the predictions of stellar evolutionary theory. However, in recent years our understanding of RSGs, including the models and methods applied to our observations and interpretations of these stars, has changed and grown dramatically. This review looks back at some of the latest work that has progressed our understanding of RSGs, and considers the many new questions posed by our ever-evolving picture of these cool massive stars.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Dusty stellar sources classification by implementing machine learning methods based on spectroscopic observations in the Magellanic Clouds

    astro-ph.GA 2025-04 conditional novelty 4.0 of 10

    A probabilistic random forest trained on 618 spectroscopically confirmed dusty stars achieves 89% accuracy and relabels more than 23,000 sources through a consensus of four models.

  2. Machine Learning Classification of Young Stellar Objects and Evolved Stars in the Magellanic Clouds Using the Probabilistic Random Forest Classifier

    astro-ph.GA 2025-04 conditional novelty 3.0 of 10

    A Probabilistic Random Forest classifies 618 Magellanic Cloud dusty stars into five stellar classes with 89% reported accuracy, though crucial validation details are missing.

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