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首页> 外文期刊>The Astrophysical journal >Estimation of Carbon Abundances in Metal-Poor Stars. I. Application to the Strong G-Band Stars of Beers, Preston, and Shectman
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Estimation of Carbon Abundances in Metal-Poor Stars. I. Application to the Strong G-Band Stars of Beers, Preston, and Shectman

机译:贫金属星中碳丰度的估算。 I.应用于啤酒,普雷斯顿和谢克曼的强G波段星

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We develop and test a method for the estimation of metallicities ([Fe/H]) and carbon abundance ratios ([C/Fe]) for carbon-enhanced metal-poor (CEMP) stars based on the application of artificial neural networks, regressions, and synthesis models to medium-resolution (1–2 ?) spectra and J - K colors. We calibrate this method by comparison with metallicities and carbon abundance determinations for 118 stars with available high-resolution analyses reported in the recent literature. The neural network and regression approaches make use of a previously defined set of line-strength indices quantifying the strength of the Ca II K line and the CH G band, in conjunction with J - K colors from the Two Micron All Sky Survey Point Source Catalog. The use of near-IR colors, as opposed to broadband B - V colors, is required because of the potentially large affect of strong molecular carbon bands on bluer color indices. We also explore the practicality of obtaining estimates of carbon abundances for metal-poor stars from the spectral information alone, i.e., without the additional information provided by photometry, as many future samples of CEMP stars may lack such data. We find that although photometric information is required for the estimation of [Fe/H], it provides little improvement in our derived estimates of [C/Fe], and hence, estimates of carbon-to-iron ratios based solely on line indices appear sufficiently accurate for most purposes. Although we find that the spectral synthesis approach yields the most accurate estimates of [C/Fe], in particular for the stars with the strongest molecular bands, it is only marginally better than is obtained from the line index approaches. Using these methods we are able to reproduce the previously measured [Fe/H] and [C/Fe] determinations with an accuracy of ≈0.25 dex for stars in the metallicity interval -5.5 ≤ [Fe/H] ≤ -1.0 and with 0.2 ≤ (J - K)0 ≤ 0.8. At higher metallicity, the Ca II K line begins to saturate, especially for the cool stars in our program, and hence, this approach is not useful in some cases. As a first application, we estimate the abundances of [Fe/H] and [C/Fe] for the 56 stars identified as possibly carbon-rich, relative to stars of similar metal abundance, in the sample of "strong G-band" stars discussed by Beers, Preston, and Shectman.
机译:我们开发并测试了一种基于人工神经网络,回归分析的碳增强金属贫乏(CEMP)恒星估算金属度([Fe / H])和碳丰度比([C / Fe])的方法,并建立中分辨率(1-2?)光谱和J-K颜色的合成模型。我们通过与118颗恒星的金属性和碳丰度测定进行比较,并用最新文献中报道的可用高分辨率分析对这种方法进行校准。神经网络和回归方法利用先前定义的一组线强度指数,结合Ca 2 K线和CH G波段的强度,并结合了“两微米全天候测量点”来源目录中的J-K颜色。与宽带B-V颜色相反,需要使用近红外颜色,因为强分子碳带可能会对蓝色指数产生较大影响。我们还探讨了仅从光谱信息中获取贫金属恒星碳丰度估算值的实用性,即没有光度学提供的其他信息,因为许多未来的CEMP恒星样本可能缺少此类数据。我们发现尽管估算[Fe / H]需要光度信息,但它对我们得出的[C / Fe]估算值几乎没有改善,因此,仅基于线指数的碳铁比估算值出现了在大多数情况下都足够准确。尽管我们发现光谱合成方法可以得出[C / Fe]的最准确估计值,尤其是对于具有最强分子带的恒星,但它仅比通过线指数方法获得的结果略好。使用这些方法,我们能够重现先前测量的[Fe / H]和[C / Fe]测定值,对于金属度范围为-5.5≤[Fe / H]≤-1.0和0.2的恒星,精确度约为0.25 dex。 ≤(J-K)0≤0.8。在较高的金属含量下,Ca II K线开始饱和,尤其是对于我们程序中的冷恒星,因此,这种方法在某些情况下无效。作为第一个应用,我们估计“强G波段”样本中相对于类似金属丰度的恒星,被确定为可能富含碳的56个恒星的[Fe / H]和[C / Fe]丰度。比尔斯,普雷斯顿和谢克曼讨论过的星星。

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