Mock Image Training Sets for DeepMerge ("DEEPMERGE")

To investigate the use of Convolutional Neural Networks (CNNs) for distinguishing between simulated distant (z=2) merging and non-merging galaxies, the authors created two versions of mock data mimicking Hubble Space Telescope and James Webb Space Telescope observations: pristine (simulated galaxy images with PSF blurring) and noisy (simulated galaxy images with PSF and observational noise). The accuracy of the CNN model on the test set is 79% (76%) on the pristine (noisy) mock data. The CNN outperforms a Random Forest classifier (Snyder et al. 2019), which was shown to be superior to conventional one- or two-dimensional statistical methods (Concentration, Asymmetry, the Gini, M20 statistics etc.), which are commonly used when classifying merging galaxies. These data were derived from the z=2 snapshot of the Illustris-1 simulation from the Illustris Project.

为探究卷积神经网络（Convolutional Neural Networks, CNNs）在区分模拟红移z=2的遥远并合星系与非并合星系中的应用价值，研究者构建了两套复刻哈勃空间望远镜与詹姆斯·韦布空间望远镜观测结果的模拟数据集版本：纯净版（仅含点扩散函数（Point Spread Function, PSF）模糊效应的模拟星系图像）与含噪版（同时包含点扩散函数模糊与观测噪声的模拟星系图像）。该卷积神经网络模型在测试集上的分类准确率为：纯净模拟数据集下79%，含噪模拟数据集下76%。此卷积神经网络的分类性能优于随机森林分类器（Snyder等，2019），而后者已被证实优于传统的一维或二维统计方法（如集中度、非对称性、基尼系数、M20统计量等），此类方法是并合星系分类任务中的常用手段。本数据集源自Illustris项目中Illustris-1模拟的z=2快照数据。