We investigate and demonstrate the use of convolutional neural networks (CNNs) for the task of distinguishing between merging and non-merging galaxies in simulated images, and for the first time at high redshifts (i.e., $z=2$). We extract images of merging and non-merging galaxies from the Illustris-1 cosmological simulation and apply observational and experimental noise that mimics that from the Hubble Space Telescope; the data without noise form a “pristine” data set and that with noise form a “noisy” data set. The test set classification accuracy of the CNN is 79% for pristine and 76% for noisy. The CNN outperforms a Random Forest classifier, which was shown to be superior to conventional one- or two-dimensional statistical methods (Concentration, Asymmetry, the Gini, $M_{20}$ statistics etc.), which are commonly used when classifying merging galaxies. We also investigate the selection effects of the classifier with respect to merger state and star formation rate, finding no bias. Finally, we extract Grad-CAMs (Gradient-weighted Class Activation Mapping) from the results to further assess and interrogate the fidelity of the classification model.

我们调查并演示了卷积神经网络（CNN）在区分模拟图像中合并星系和非合并星系以及首次在高红移（即 $ž=2$）。我们从Illustris-1宇宙模拟中提取正在合并和未合并的星系的图像，并应用类似于哈勃太空望远镜的观测和实验噪声；没有噪声的数据形成一个“原始”数据集，而有噪声的数据形成一个“嘈杂”数据集。CNN的测试集分类准确性（原始）为79％，噪声为76％。CNN优于随机森林分类器，事实证明该分类器优于传统的一维或二维统计方法（浓度，不对称，基尼，${\mathrm{中号}}_{20}$统计数据等），这在对合并星系进行分类时通常使用。我们还研究了分类器在合并状态和星级形成率方面的选择效果，没有发现偏差。最后，我们从结果中提取Grad-CAM（梯度加权类激活映射），以进一步评估和询问分类模型的保真度。