论文标题
深度学习的星系簇的质量估计I：Sunyaev-Zel'Dovich效应
Mass Estimation of Galaxy Clusters with Deep Learning I: Sunyaev-Zel'dovich Effect
论文作者
论文摘要
我们提出了一种深入学习的新应用，以直接从微波天空的图像中推断出星系簇的质量。有效地，这是确定群集的Sunyaev-Zel'Dovich（SZ）效应信号和质量之间的缩放关系的新方法。使用的深度学习算法是Mresunet，它是一种修改的进料深度学习算法，该算法将残留的学习，卷积层与不同的扩张率，图像回归激活和U-NET框架结合在一起。我们使用微波天空的模拟图像来训练和测试深度学习模型，其中包括来自宇宙微波背景（CMB），尘土和射电星系，仪器噪声以及群集自己的SZ信号的信号。模拟集群样品涵盖了质量范围1 $ \ times 10^{14}〜\ rm m _ {\ odot} $ $ <m_ {200 \ rm c} <$ 8 $ 8 $ \ times 10^{14}〜\ rm m _ {\ rm m _ {\ odot} $ at $ z = 0.7 $。受过训练的模型估计了群集质量的1 $σ$不确定性$Δm/m \ leq 0.2 $，与SZ信号上的输入散射一致。我们验证该模型即使在使用磁性的流体动力学模拟对方位对称的SZ轮廓进行训练时，该模型即使对现实的SZ曲线起作用。
We present a new application of deep learning to infer the masses of galaxy clusters directly from images of the microwave sky. Effectively, this is a novel approach to determining the scaling relation between a cluster's Sunyaev-Zel'dovich (SZ) effect signal and mass. The deep learning algorithm used is mResUNet, which is a modified feed-forward deep learning algorithm that broadly combines residual learning, convolution layers with different dilation rates, image regression activation and a U-Net framework. We train and test the deep learning model using simulated images of the microwave sky that include signals from the cosmic microwave background (CMB), dusty and radio galaxies, instrumental noise as well as the cluster's own SZ signal. The simulated cluster sample covers the mass range 1$\times 10^{14}~\rm M_{\odot}$ $<M_{200\rm c}<$ 8$\times 10^{14}~\rm M_{\odot}$ at $z=0.7$. The trained model estimates the cluster masses with a 1 $σ$ uncertainty $ΔM/M \leq 0.2$, consistent with the input scatter on the SZ signal of 20%. We verify that the model works for realistic SZ profiles even when trained on azimuthally symmetric SZ profiles by using the Magneticum hydrodynamical simulations.
