The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has produced massive medium-resolution spectra. Data mining for special and rare stars in massive LAMOST spectra is of great significance. Feature extraction plays an important role in the process of automatic spectra classification. The proper classification network can extract most of the common spectral features with minimum noise and individual features. Such a network has better generalization capabilities and can extract sufficient features for classification. A variety of classification networks of one dimension and two dimensions are both designed and implemented systematically in this paper to verify whether spectra is easier to deal with in a 2D situation. The experimental results show that the fully connected neural network cannot extract enough features. Although convolutional neural network (CNN) with a strong feature extraction capability can quickly achieve satisfactory results on the training set, there is a tendency for overfitting. Signal-to-noise ratios also have effects on the network. To investigate the problems above, various techniques are tested and the enhanced multi-scale coded convolutional neural network (EMCCNN) is proposed and implemented, which can perform spectral denoising and feature extraction at different scales in a more efficient manner. In a specified search, eight known and one possible cataclysmic variables (CVs) in LAMOST MRS are identified by EMCCNN including four CVs, one dwarf nova and three novae. The result supplements the spectra of CVs. Furthermore, these spectra are the first medium-resolution spectra of CVs. The EMCCNN model can be easily extended to search for other rare stellar spectra.

中文翻译：

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基于增强型多尺度卷积神经网络的质谱自动分类

大天空区多目标光纤光谱望远镜（LAMOST）已产生大量中等分辨率的光谱。LAMOST光谱中特殊和稀有恒星的数据挖掘具有重要意义。特征提取在光谱自动分类过程中起着重要作用。适当的分类网络可以以最小的噪声和单个特征提取大多数公共频谱特征。这样的网络具有更好的泛化能力，并且可以提取足够的特征用于分类。本文系统地设计和实现了多种一维和二维分类网络，以验证在二维情况下光谱是否更易于处理。实验结果表明，完全连接的神经网络无法提取足够的特征。尽管具有强大特征提取能力的卷积神经网络（CNN）可以在训练集上快速获得令人满意的结果，但存在过度拟合的趋势。信噪比也会对网络产生影响。为了研究上述问题，测试了各种技术，并提出并实现了增强的多尺度编码卷积神经网络（EMCCNN），该网络可以更有效地在不同尺度上执行频谱去噪和特征提取。在指定的搜索中，EMCCNN识别了LAMOST MRS中的八个已知和一个可能的催化变量（CV），包括四个CV，一个矮新星和三个新星。结果补充了CV的光谱。此外，这些光谱是CV的第一个中分辨率光谱。