通过卷积运算提取白矮主序双星的光谱特征是提高识别精度的有效手段。 通过设计一维卷积神经网络, 以判别学习的方式从大量混合光谱中拟合出具有稳定分布的12个卷积核, 有效提取白矮主序双星的卷积特征。 通过引入相对松弛的光谱类别先验分布, 提出反贝叶斯学习策略以解决由于光谱抽样有偏带来的问题, 显著提高识别精度。 通过比较光谱在不同信噪比下的交叉熵测试误差, 分析卷积特征的提取过程对光谱信噪比的鲁棒性。 实验发现, 基于反贝叶斯学习策略的一维卷积神经网络对白矮主序双星的识别准确率达到99.0(±0.3), 超过了经典的PCA+SVM模型。 卷积特征谱的池化过程以降低光谱分辨率的形式缓解了光谱噪声对识别精度的影响。 当信噪比小于3时, 必须通过增加模型在光谱上的迭代次数以形成稳定的卷积核; 当信噪比介于3与6之间时, 光谱卷积特征较为稳定; 当信噪比大于6时, 光谱卷积特征的稳定性显著上升, 信噪比对于模型识别精度带来的影响可以忽略。

In the task of White Dwarf+Main Sequence (WDMS) finding in massive spectral data release, convolution can significantly improve the classification accuracy by extracting hierarchical, translational-invariant features. In this paper, by designing one dimensional convolutional neural network (1-D CNN) which was further trained in a discriminative, supervised way, 12 kernels with stable numerical distributions were produced, helping to generate spectral feature maps of WDMS. To solve the problem brought by biased sampling in the WDMS training set, we proposed a learning principle called Anti-Bayesian Learning Paradigm (ALP) which was built on the basis of order statistics by implying a comparatively looser prior distribution of spectral types. And in the way of separating training spectra into several groups according to their signal-to-noise ratios (SNR), we analyzed the robustness of convolutional extraction process to spectral noise. Experimental results indicated that, (1) WDMS classification with 1-D CNN and ALP reached the accuracy of 99.0%±0.3%, which outperformed the classic PCA+SVM model. (2) Pooling after convolution operations relieved the negative impact of spectral noise by lowering resolution. (3) When the SNR was less than 3, more epochs were required to learn stable kernels; when the SNR was between 3 and 6, the spectral convolutional features was stable; when the SNR was greater than 6, the convolution process acquired higher stability to eliminate the negative impact of SNR on model performance.