机器学习检测恒星考古学数据中第一颗恒星的多样性
Machine learning detects multiplicity of the first stars in stellar archaeology data
摘要：
在揭示第一批恒星的性质时，主要的天文学线索是第二代恒星的元素组成，在我们的银河系中观察到的极贫金属（EMP）恒星。然而，对于它们的多重性没有观测限制，这对于理解星系形成的早期阶段至关重要。我们开发了一种新的数据驱动的方法来分类观测到的EMP星单或多富集星与支持向量机。我们还使用我们自己的核合成产额的核心坍缩超新星的混合回落，可以解释许多观察到的EMP星。我们的方法预测，第一次，$31.8\% \pm 2.3\%$的462分析EMP星被归类为单富集。这意味着大多数EMP恒星可能是多重富集的，这表明第一批恒星诞生于小星团中。较低金属丰度的恒星更有可能被一颗超新星富集，其中大多数都有高碳增强。Fe、Mg. Ca和C是该分类的最具信息性的元素。此外，尽管氧气的可观测性很低，但它的信息量很大。我们的数据驱动方法为解决银河系考古调查复杂数据集中第一颗恒星的奥秘提供了新的思路。
英文摘要：
In unveiling the nature of the first stars, the main astronomical clue is the elemental compositions of the second generation of stars, observed as extremely metal-poor (EMP) stars, in our Milky Way Galaxy. However, no observational constraint was available on their multiplicity, which is crucial for understanding early phases of galaxy formation. We develop a new data-driven method to classify observed EMP stars into mono- or multi-enriched stars with Support Vector Machines. We also use our own nucleosynthesis yields of core-collapse supernovae with mixing-fallback that can explain many of observed EMP stars. Our method predicts, for the first time, that $31.8\% \pm 2.3\%$ of 462 analyzed EMP stars are classified as mono-enriched. This means that the majority of EMP stars are likely multi-enriched, suggesting that the first stars were born in small clusters. Lower metallicity stars are more likely to be enriched by a single supernova, most of which have high carbon enhancement. We also find that Fe, Mg. Ca, and C are the most informative elements for this classification. In addition, oxygen is very informative despite its low observability. Our data-driven method sheds a new light on solving the mystery of the first stars from the complex data set of Galactic archaeology surveys.
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