论文标题
深度学习的星系簇的质量估计II：CMB群集镜头
Mass Estimation of Galaxy Clusters with Deep Learning II: CMB Cluster Lensing
论文作者
论文摘要
我们提出了深度学习的新应用，以从微波天空的图像中重建宇宙微波背景（CMB）温度图，并使用这些重建的地图来估计星系簇的质量。我们使用馈送深度学习网络Mresunet进行分析的两个步骤。第一个深度学习模型Mresunet-I经过训练，可以从微波天空的一系列模拟图像中重建前景和噪声抑制CMB地图，其中包括来自宇宙微波背景的信号，天文学前景，尘土和宽度星系，乐器噪声，乐器噪声以及群集的热量Sunaev Zelev Zelovich Zelovich Signal。第二个深度学习模型Mresunet-II经过训练，可以从重建的前景中的重力镜头标志和噪声抑制的CMB图中估算簇质量。对于类似于SPTPOL的噪声水平，经过训练的Mresunet-II模型以$ 10^4 $ GALAXY群集样品的恢复质量，使用1- $σ$不确定性$Δm_ {\ rm 200c}^{\ rm 200c}^{\ rm est} {\ rm est}/m _ { $ m _ {\ rm 200c}^{\ rm true} = 10^{14}〜\ rm m _ {\ odot} $和$ 8 \ times 10^{14}〜\ rm m _ {\ odot} $。我们还测试了回收质量的潜在偏差，发现对于一组$ 10^5 $簇，估算器恢复$ M _ {\ rm 200c}^{\ rm est} = 2.02 \ times 10^{14}〜\ rm m m _ {\ rm m _ {\ odot} $，在1％的intup上一致。潜在偏差的2 $σ$上限为3.5％。
We present a new application of deep learning to reconstruct the cosmic microwave background (CMB) temperature maps from the images of microwave sky, and to use these reconstructed maps to estimate the masses of galaxy clusters. We use a feed-forward deep learning network, mResUNet, for both steps of the analysis. The first deep learning model, mResUNet-I, is trained to reconstruct foreground and noise suppressed CMB maps from a set of simulated images of the microwave sky that include signals from the cosmic microwave background, astrophysical foregrounds like dusty and radio galaxies, instrumental noise as well as the cluster's own thermal Sunyaev Zel'dovich signal. The second deep learning model, mResUNet-II, is trained to estimate cluster masses from the gravitational lensing signature in the reconstructed foreground and noise suppressed CMB maps. For SPTpol-like noise levels, the trained mResUNet-II model recovers the mass for $10^4$ galaxy cluster samples with a 1-$σ$ uncertainty $ΔM_{\rm 200c}^{\rm est}/M_{\rm 200c}^{\rm est} =$ 0.108 and 0.016 for input cluster mass $M_{\rm 200c}^{\rm true}=10^{14}~\rm M_{\odot}$ and $8\times 10^{14}~\rm M_{\odot}$, respectively. We also test for potential bias on recovered masses, finding that for a set of $10^5$ clusters the estimator recovers $M_{\rm 200c}^{\rm est} = 2.02 \times 10^{14}~\rm M_{\odot}$, consistent with the input at 1% level. The 2 $σ$ upper limit on potential bias is at 3.5% level.
