Machine Learning Detects Multiplicity of the First Stars in Stellar Archaeology Data
Machine Learning Detects Multiplicity of the First Stars in Stellar Archaeology Data
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DOI：
10.3847/1538-4357/acbcc6
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发表时间：
2023-02
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影响因子：
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通讯作者：
T. Hartwig;M. Ishigaki;C. Kobayashi;N. Tominaga;K. Nomoto
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文献类型：
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作者：
T. Hartwig;M. Ishigaki;C. Kobayashi;N. Tominaga;K. Nomoto
在揭示第一代恒星的本质时，主要的天文学线索是银河系中第二代恒星的元素组成，这些恒星被观测为极度贫金属（EMP）恒星。然而，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方法，利用支持向量机将观测到的 EMP 恒星分类为单富集或多富集恒星。我们还使用我们自己的核心塌陷超新星的核合成产量以及混合回落，可以解释许多观察到的电磁脉冲恒星。我们的方法首次预测，462 颗分析的 EMP 恒星中有 31.8% ± 2.3% 被归类为单一富集恒星。这意味着大多数电磁脉冲恒星可能是多重浓缩的，这表明第一批恒星诞生于小星团中。金属丰度较低的恒星更有可能被单个超新星富集，其中大多数具有高碳增强性。我们还发现了Fe、Mg。 Ca 和 C 是该分类中信息最丰富的元素。此外，尽管氧气的可观测性较低，但其信息量却很大。我们的数据驱动方法为从银河考古调查的复杂数据集中解开第一批恒星之谜提供了新的线索。
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 the Milky Way. 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 the observed EMP stars. Our method predicts, for the first time, that 31.8% ± 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 archeology surveys.