IllustrisTNG星系团普查的深度学习视图
A deep learning view of the census of galaxy clusters in IllustrisTNG
摘要：
星系团多样性的起源仍然是大尺度结构形成和星系团演化的一个未解释的方面。我们提出了一种新的方法，使用X射线图像来识别冷核（CC），弱冷核（WCC）和非冷核（NCC）星系团，这是由他们的中心冷却时间定义。我们使用卷积神经网络ResNet-18来对聚类进行分类，该网络通常用于图像分析。我们产生模拟钱德拉X射线观测的样本318大规模集群从IllustrisTNG模拟。该网络使用低分辨率的模拟Chandra图像进行训练和测试，覆盖我们样本中集群的中心1 Mpc正方形。在没有任何光谱信息的情况下，深度学习算法能够识别CC、WCC和NCC聚类，分别达到92%、81%和83%的平衡准确度（BAcc）。该性能优于使用中心气体密度的常规方法的分类，平均BAcc = 81%，或表面亮度浓度，给出BAcc = 73%。我们使用类激活映射来定位分类决策的判别区域。从这个分析中，我们观察到网络已经利用从聚类中心到r~300 kpc和r~500 kpc的区域分别识别CC和NCC聚类。它可能已经认识到的功能，在集群内的介质，是与活动星系核的反馈和破坏性的主要合并。
英文摘要：
The origin of the diverse population of galaxy clusters remains an unexplained aspect of large-scale structure formation and cluster evolution. We present a novel method of using X-ray images to identify cool core (CC), weak cool core (WCC), and non cool core (NCC) clusters of galaxies, that are defined by their central cooling times. We employ a convolutional neural network, ResNet-18, which is commonly used for image analysis, to classify clusters. We produce mock Chandra X-ray observations for a sample of 318 massive clusters drawn from the IllustrisTNG simulations. The network is trained and tested with low resolution mock Chandra images covering a central 1 Mpc square for the clusters in our sample. Without any spectral information, the deep learning algorithm is able to identify CC, WCC, and NCC clusters, achieving balanced accuracies (BAcc) of 92%, 81%, and 83%, respectively. The performance is superior to classification by conventional methods using central gas densities, with an average BAcc = 81%, or surface brightness concentrations, giving BAcc = 73%. We use Class Activation Mapping to localize discriminative regions for the classification decision. From this analysis, we observe that the network has utilized regions from cluster centers out to r~300 kpc and r~500 kpc to identify CC and NCC clusters, respectively. It may have recognized features in the intracluster medium that are associated with AGN feedback and disruptive major mergers.
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