MaStar中的富碳和富氧恒星:识别和分类

L. Hill, C. Maraston, Daniel Thomas, R. Yan, Yanping Chen, G. Stringfellow, Richard R. Lane, J. G. Fernández-Trincado
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引用次数: 0

摘要

在恒星演化的热脉动渐变巨枝(TP-AGB)阶段,富碳和富氧恒星是∼1 Gyr恒星群光谱的相关贡献者。由于复杂的分子和质量损失效应,这些类型的大气模型并不确定。因此,经验光谱至关重要,但由于 TP-AGB 的寿命较短(∼3 Myr),因此样本较少。在这里,我们利用庞大的 MaNGA 恒星库 MaStar(∼60,000 个光谱)来识别富含 C、O 型的恒星。我们定义了一种光学颜色选择,其切点为(g - r)> 2和(g - i)< 1.55(g - r) - 0.07,并用已知的富C和O型光谱进行校准。这样就可以沿着干净、分离的序列识别出富含 C、O 的恒星。在 V、R、I 波段中也有类似的选择。我们的公式识别出的富 C 和富 O 光谱的预测性能指标 F1 分数分别为 0.72 和 0.74(大于 1)。我们最终在 MaStar 中识别出 41 颗富含 C 和 87 颗富含 O 的 AGB 型恒星,其中 5 颗和 49 颗没有与 SIMBAD 相对应的恒星。我们还探测到了一个非AGB型矮C星样本。我们进一步设计了一个拟合程序,通过使用经验C型和富O型光谱作为拟合模板,将光谱划分为宽光谱类型。我们发现大多数候选者的拟合效果都非常好,并按照现有的分类方法将它们分为富C和富O两类,这与有效温度相关。我们的选择模型可应用于大型测光巡天(如 Euclid 和 Rubin)。分类光谱将有助于未来的演化种群合成模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Carbon- and Oxygen-rich stars in MaStar: identification and classification
Carbon- and Oxygen-rich stars populating the Thermally-Pulsing Asymptotic Giant Branch (TP-AGB) phase of stellar evolution are relevant contributors to the spectra of ∼1 Gyr old populations. Atmosphere models for these types are uncertain, due to complex molecules and mass-loss effects. Empirical spectra are then crucial, but samples are small due to the short (∼3 Myr) TP-AGB lifetime. Here we exploit the vastness of the MaNGA Stellar library MaStar (∼60, 000 spectra) to identify C,O-rich type stars. We define an optical colour selection with cuts of (g − r) > 2 and (g − i) < 1.55(g − r) − 0.07, calibrated with known C- and O- rich spectra. This identifies C-,O-rich stars along clean, separated sequences. An analogue selection is found in V, R, I  bands. Our equation identifies C-rich and O-rich spectra with predictive performance metric F1-scores of 0.72 and 0.74 (over 1), respectively. We finally identify 41 C- and 87 O-rich type AGB stars in MaStar, 5 and 49 of which do not have a SIMBAD counterpart. We also detect a sample of non-AGB, dwarf C-stars. We further design a fitting procedure to classify the spectra into broad spectral types, by using as fitting templates empirical C and O-rich spectra. We find remarkably good fits for the majority of candidates and categorise them into C- and O-rich bins following existing classifications, which correlate to effective temperature. Our selection models can be applied to large photometric surveys (e.g. Euclid, Rubin). The classified spectra will facilitate future evolutionary population synthesis models.
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