Automatic spectral classification of stellar spectra with low signal-to-noise ratio using artificial neural networks
Automatic spectral classification of stellar spectra with low signal-to-noise ratio using artificial neural networks
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DOI：
10.1051/0004-6361/201016422
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发表时间：
2012-02
影响因子：
6.5
通讯作者：
S. Navarro;R. Corradi;A. Mampaso
中科院分区：
文献类型：
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作者：
S. Navarro;R. Corradi;A. Mampaso
上下文作为一个旨在推导35个行星状星云的距离的项目的一部分，分析了几千颗恒星的光谱，以确定它们的光谱类型和光度等级。目标。我们在这里介绍的自动光谱分类过程中用于恒星光谱分类。该系统可用于任何其他恒星光谱相似或更高的信噪比进行分类。方法.光谱分类使用人工神经网络系统进行训练的一组线强度指数中选择的光谱线最敏感的温度和最好的光度示踪剂。的神经网络的训练和验证过程进行了讨论，并提出了额外的验证探针，旨在确保光谱分类的准确性的结果。结果我们的系统允许分类的恒星光谱的信号噪声比（S /N）显着低于它通常被认为是需要的。当信噪比≥ 20时，其精度一般优于两个光谱子类型。在S /N < 20时，分类仍然是可能的，但精度较低。它的潜力，以确定特殊的来源，如发射线的明星，也被承认。
Context. As part of a project aimed at deriving extinction-distances for thirty-five planetary nebulae, spectra of a few thousand stars were analyzed to determine their spectral type and luminosity class. Aims. We present here the automatic spectral classification process used to classify stellar spectra. This system can be used to classify any other stellar spectra with similar or higher signal-to-noise ratios. Methods. Spectral classification was performed using a system of artificial neural networks that were trained with a set of line-strength indices selected among the spectral lines most sensitive to temperature and the best luminosity tracers. The training and validation processes of the neural networks are discussed and the results of additional validation probes, designed to ensure the accuracy of the spectral classification, are presented. Results. Our system permits the classification of stellar spectra of signal-to-noise ratio (S /N) significantly lower than it is generally considered to be needed. For S /N ≥ 20, a precision generally better than two spectral subtypes is obtained. At S /N < 20, classification is still possible but has a lower precision. Its potential to identify peculiar sources, such as emission-line stars, is also recognized.