Stellar spectral models compared with empirical data
Stellar spectral models compared with empirical data
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DOI：
10.1093/mnras/stz754
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发表时间：
2019-03
影响因子：
4.8
通讯作者：
A. T. Knowles;A. Sansom;Paula Coelho;C. A. Prieto;Charlie Conroy;A. Vazdekis
中科院分区：
文献类型：
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作者：
A. T. Knowles;A. Sansom;Paula Coelho;C. A. Prieto;Charlie Conroy;A. Vazdekis
经验MILES恒星库被用来测试三种不同的，国家的最先进的，恒星光谱的理论模型库的准确性。这些模型被广泛用于恒星人口分析的文献。使用差分方法，以便测试元素丰度变化的响应，而不是理论光谱的绝对水平。首先，我们直接比较模型线强度和光谱的经验数据，以调查趋势。然后，我们测试如何以及线的强度匹配时，元素响应函数被用来解释[α/Fe]丰度的变化。其目的是找出模型最能代表真实的星星光谱，在一个不同的方式，从而确定良好的选择模型中使用恒星人口分析涉及丰度模式。我们发现，大多数谱线强度很好地代表了这些模型，特别是铁和钠敏感指数。其中包括高阶Balmer线（Hδ，Hγ），模型显示出比数据更多的变化，特别是在低温下。与观测结果相比，模型系统性地低估了C24668。我们发现，这些模型之间的差异通常不如模型与数据之间的差异显著。确定了对一组模型的C2行列表的更正，以供将来使用。
The empirical MILES stellar library is used to test the accuracy of three different, state-of-the-art, theoretical model libraries of stellar spectra. These models are widely used in the literature for stellar population analysis. A differential approach is used so that responses to elemental abundance changes are tested rather than absolute levels of the theoretical spectra. First we directly compare model line strengths and spectra to empirical data to investigate trends. Then we test how well line strengths match when element response functions are used to account for changes in [α/Fe] abundances. The aim is to find out where models best represent real star spectra, in a differential way, and hence identify good choices of models to use in stellar population analysis involving abundance patterns. We find that most spectral line strengths are well represented by these models, particularly iron and sodium sensitive indices. Exceptions include the higher order Balmer lines (Hδ, Hγ), in which the models show more variation than the data, particularly at low temperatures. C24668 is systematically underestimated by the models compared to observations. We find that differences between these models are generally less significant than the ways in which models vary from the data. Corrections to C2 line lists for one set of models are identified, improving them for future use.