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From evolved Long-Period-Variable stars to the evolution of M31
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One of the ways to understand the genesis and evolution of the universe is to know how galaxies have formed and evolved. In this regard, the study of star formation history (SFH) plays an important role in the accurate understanding of galaxies. In this paper, we used long-period variable stars (LPVs) to estimate the SFH in the Andromeda galaxy (M31). These cool stars reach their peak luminosity in the final stage of their evolution; their birth mass is directly related to their luminosity. Therefore, we construct the mass function and the star formation history using stellar evolution models.
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Cited by 1 Pith paper
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Machine Learning Classification of Young Stellar Objects and Evolved Stars in the Magellanic Clouds Using the Probabilistic Random Forest Classifier
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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