天体光谱是天体物理学重要的研究对象, 通过光谱可以获取天体的许多物理、 化学参数如有效温度、 金属丰度、 表面重力加速度和视向速度等。 白矮主序双星是一类致密的双星系统, 对研究致密双星的演化特别是公共包层的演化有着重要的意义。 国内外的大型巡天望远镜如美国斯隆望远镜以及中国的郭守敬望远镜, 每天都产生大量光谱数据。 如此海量的光谱数据无法完全用人工进行分析。 因此, 使用机器学习方法从海量的天体光谱中自动搜索白矮主序双星光谱, 有着非常现实的意义。 目前的光谱自动识别方法主要通过对已有的标签样本进行分析, 通过训练得到分类器, 再对未知目标进行识别。 这类方法对样本的数量有明确的要求。 白矮主序双星的实测光谱数量有限。 若要通过有限的样本集准确学习白矮主序双星的光谱特征, 不仅需要扩大样本数量, 还需要提高特征提取和分类算法的精度。 在前期工作中, 通过机器学习等方法在海量巡天数据中识别了一批白矮主序双星的光谱, 为该实验提供了数据源。 使用对抗神经网络生成新的白矮主序双星光谱, 扩大训练数据量至原数据集约两倍的数量, 增强了分类模型的泛化能力。 通过反贝叶斯学习修正损失函数, 将损失函数的大小与样本的方差相关联, 抑制了异常数据对模型造成的影响, 提升了模型的鲁棒性, 解决了由于训练样本集偏差带来的梯度消失以及训练陷入局部最优解等问题。 该实验基于Tensorflow深度学习库。 使用Tensorflow搭建的生成对抗网络具有较好的鲁棒性, 并且封装了内部实现细节, 使得算法得以更好地实现。 除此之外, 由Tensorflow搭建的卷积神经网络在该实验中用于分类准确度测试。 实验结果表明, 二维卷积神经网络能够利用卷积核有效地提取白矮主序双星的卷积特征并进行分类。 基于反贝叶斯学习策略的卷积神经网络分类器在白矮主序双星原始数据及对抗神经网络生成光谱的识别任务中达到了约98.3%的准确率。 该方法也可用于在巡天望远镜的海量光谱中搜索其他特殊和稀少天体如激变变星、 超新星等。

Astronomical spectrum is an important research object in astrophysics. Many physical and chemical properties such as effective temperature, metal abundance, surface gravity and radial velocity can be inferred according to the spectra. The white dwarf main sequence binary star (WDMS) is a kind of binary star system, which is of great significance to the study of the evolution of binary stars, especially the evolution of post-common envelope. Domestic and foreign survey telescopes such as SDSS and LAMOST generate massive spectral data every day and such a large amount of spectral data cannot be analyzed manually. Therefore, it is very practical to use the machine learning method to automatically search for the WDMS spectra from the massive survey spectra. Current automatic spectral identification methods mainly depends on the existing labeled samples. Nevertheless, the number of WDMS spectra is limited. To accurately study the spectral features of WDMS spectra through a limited sample set, it is necessary to increase the number of samples and improve the accuracy of the feature extraction algorithm simultaneously. In the previous work, a batch of WDMS spectra was identified through machine learning methods in the sky survey data, providing data source for the experiment. In this paper, the generative adversarial network (GAN) is used to generate new WDMS spectra and expand the training data volume to about twice the original data set, which enhances the generalization ability of the classification model. By modifying the loss function by Anti-Bayesian learning method, the value of the loss function is correlated with the variance of the sample, which suppresses the influence of abnormally large data on the model. It improves the robustness of the model and solves the problems like vanishing gradient and getting stuck in a local optimal solution caused by the deviation of the training sample. The experiments in this paper are based on the Tensorflow deep learning library. The GAN built by Tensorflow is robust and encapsulates the internal implementation details, making the algorithm itself better represented. In addition, the Convolutional Neural Network (CNN) built by Tensor flow was used in this experiment for classification accuracy testing. The experimental results show that the two-dimensional convolutional neural network can use the convolution kernel to effectively extract the convolution characteristics of WDMS spectra and classify them. The convolutional neural network classifier based on the anti-Bayesian learning strategy achieves an accuracy of about 98.3% in the identification task of original WDMS spectra and GAN generated data. The method can also be used to search for other specific targets such as cataclysmic variable stars or supernova in the massive spectra of the telescope.