使用深度学习对星系的宇宙结构进行分类：将宇宙学模拟与观测相结合
Classification of cosmic structures for galaxies with deep learning: connecting cosmological simulations with observations
摘要：
我们探索了深度学习对宇宙结构进行分类的能力。在宇宙学模拟中，根据暗物质的分布和运动学特征，将宇宙体分割为空洞、片、丝和结，并根据分割结果对星系进行分类。然而，观察性研究不能采用这种使用DM的分类方法。在这项研究中，我们证明了深度学习可以弥合模拟和观察之间的差距。我们的模型基于三维卷积神经网络，并在模拟中使用星系分布的数据进行训练，以从星系而不是DM推断结构类。我们的模型可以像以前的研究一样准确地预测类别标签，使用DM分布进行训练和预测。这意味着星系分布可以取代DM的宇宙结构分类，我们的模型使用星系可以直接应用于宽视场巡天观测。当忽略观测限制时，我们的模型可以将模拟的星系分为四类，（宏观平均$F_{\rm 1}$ -分数）为64%。如果考虑限制星等等的限制，我们的模型可以将SDSS星系分类为$\sim100~{\rm Mpc}$，准确率为60%。在区分空星系和其他星系的二元分类中，我们的模型可以达到百分之八十八的准确率。
英文摘要：
We explore the capability of deep learning to classify cosmic structures. In cosmological simulations, cosmic volumes are segmented into voids, sheets, filaments and knots, according to the distribution and kinematics of dark matter (DM), and galaxies are also classified according to the segmentation. However, observational studies cannot adopt this classification method using DM. In this study, we demonstrate that deep learning can bridge the gap between simulations and observations. Our models are based on three-dimensional convolutional neural networks and trained with data of the distribution of galaxies in a simulation to deduce the structure classes from the galaxies rather than DM. Our model can predict the class labels as accurate as a previous study using DM distribution for the training and prediction. This means that galaxy distribution can be a substitution for DM for the cosmic-structure classification, and our models using galaxies can be directly applied to wide-field survey observations. When observational restrictions are ignored, our model can classify simulated galaxies into the four classes with an accuracy (macro-averaged $F_{\rm 1}$-score) of 64 per cent. If restrictions such as limiting magnitude are considered, our model can classify SDSS galaxies at $\sim100~{\rm Mpc}$ with an accuracy of 60 per cent. In the binary classification distinguishing void galaxies from the others, our model can achieve an accuracy of 88 per cent.
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