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Spectral Classification of Unresolved Binary Stars with Artificial Neural Networks

机译:人工神经网络对未解析双星的光谱分类

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An artificial neural network technique has been developed to perform two-dimensional spectral classification of the components of binary stars. The spectra are based on the 15 ? resolution near-infrared (NIR) spectral classification system described by Torres-Dodgen & Weaver. Using the spectrum with no manual intervention except wavelength registration, a single artificial neural network (ANN) can classify these spectra with Morgan-Keenan types with an average accuracy of about 2.5 types (subclasses) in temperature and about 0.45 classes in luminosity for up to 3 mag of difference in luminosity. The error in temperature classification does not increase substantially until the secondary contributes less than 10% of the light of the system. By following the coarse-classification ANN with a specialist ANN, the mean absolute errors are reduced to about 0.5 types in temperature and 0.33 classes in luminosity. The resulting ANN network was applied to seven binary stars.
机译:已经开发了一种人工神经网络技术来对双星的成分进行二维光谱分类。光谱基于15? Torres-Dodgen&Weaver描述的高分辨率近红外(NIR)光谱分类系统。使用除波长注册外无需人工干预的光谱,单个人工神经网络(ANN)可以将这些光谱分类为Morgan-Keenan类型,其平均温度精度约为2.5种(子类),发光度的平均精度约为0.45类,最高可达亮度差异为3 mag。直到次级的贡献少于系统光的10%时,温度分类的误差才会显着增加。通过遵循具有专业ANN的粗分类ANN,平均绝对误差可降低到温度的0.5种和发光度的0.33类。由此产生的人工神经网络被应用于七颗双星。

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