论文标题
卷积神经网络重建的动力学SZ检测速度
Convolutional Neural Network-reconstructed velocity for kinetic SZ detection
论文作者
论文摘要
我们报告了使用数据发行4的最新217 GHz Planck地图在Galaxy群集中的动力学Sunyaev-Zel'Dovich（KSZ）效应，具有4.9 Sigma的意义。对于检测，我们将Planck Map堆叠在Wen-Han-Liu的30,431 Galaxy Clusters Cattacter的位置。为了使KSZ信号的符号保持一致，使用机器学习方法估算了星系簇的视线速度，在该方法中，通过A卷积神经网络对群集周围的星系分布与其视线速度的关系之间的关系进行了训练。为了训练我们的网络，我们在磁性宇宙学水动力学模拟中使用了模拟的星系和星系簇。训练有素的模型应用于斯隆数字天空调查星系的大规模分布，以得出WHL星系簇的视线速度。假设对簇内培养基的标准β模型，我们获得了R500的气体质量分数为FGA，500 = 0.09 +-0.02在星系簇中，平均质量为M500〜1.0 x 10^14 msun/h。
We report the detection of the kinetic Sunyaev-Zel'dovich (kSZ) effect in galaxy clusters with a 4.9 sigma significance using the latest 217 GHz Planck map from data release 4. For the detection, we stacked the Planck map at the positions of 30,431 galaxy clusters from the Wen-Han-Liu (WHL) catalog. To align the sign of the kSZ signals, the line-of-sight velocities of galaxy clusters were estimated with a machine-learning approach, in which the relation between the galaxy distribution around a cluster and its line-of-sight velocity was trained through a convolutional neural network. To train our network, we used the simulated galaxies and galaxy clusters in the Magneticum cosmological hydrodynamic simulations. The trained model was applied to the large-scale distribution of the Sloan Digital Sky Survey galaxies to derive the line-of-sight velocities of the WHL galaxy clusters. Assuming a standard beta-model for the intracluster medium, we obtained the gas mass fraction in R500 to be fgas,500 = 0.09 +- 0.02 within the galaxy clusters with the average mass of M500 ~ 1.0 x 10^14 Msun/h.
