论文标题
CSST调查中使用贝叶斯神经网络的光度红移估算
Photometric redshift estimates using Bayesian neural networks in the CSST survey
论文作者
论文摘要
星系光度红移（照片 - $ z $）在宇宙学研究中至关重要，例如弱重力透镜和星系角簇测量。在这项工作中，我们尝试提取照片 - $ z $信息，并使用Galaxy Flux和图像数据构建其概率分布功能（PDF），并预计将由中国空间站望远镜（CSST）获得图像数据。模拟星系图像是从高级相机生成的，以进行哈勃太空望远镜（$ HST $ -ACS）和COSMOS目录进行调查，其中仔细考虑了CSST仪器效果。并使用光圈光度法从星系图像测量星系通量数据。我们构建贝叶斯多层感知器（B-MLP）和贝叶斯卷积神经网络（B-CNN），以分别预测照片-U Z $以及来自通量和图像的PDF。我们将B-MLP和B-CNN组合在一起，并构建一个混合网络，并采用转移学习技术来研究包括通量和图像数据的改进。对于带有SNR $> $ g $或$ g $或$ i $ band的Galaxy样品，我们发现photo- $ z $的准确性和离群可以实现$σ_{\ rm nmad} = 0.022 $ and $η= 2.35 \％$ n = 2.35 \％$，仅使用Flux DAGA和$ flux $ nm nm nm nm nm nm nm nm nm n。 $η= 1.32 \％$对于B-CNN仅使用图像数据。 The Bayesian hybrid network can achieve $σ_{\rm NMAD}=0.021$ and $η=1.23\%$, and utilizing transfer learning technique can improve results to $σ_{\rm NMAD}=0.019$ and $η=1.17\%$, which can provide the most confident predictions with the lowest average uncertainty.
Galaxy photometric redshift (photo-$z$) is crucial in cosmological studies, such as weak gravitational lensing and galaxy angular clustering measurements. In this work, we try to extract photo-$z$ information and construct its probability distribution function (PDF) using the Bayesian neural networks (BNN) from both galaxy flux and image data expected to be obtained by the China Space Station Telescope (CSST). The mock galaxy images are generated from the Advanced Camera for Surveys of Hubble Space Telescope ($HST$-ACS) and COSMOS catalog, in which the CSST instrumental effects are carefully considered. And the galaxy flux data are measured from galaxy images using aperture photometry. We construct Bayesian multilayer perceptron (B-MLP) and Bayesian convolutional neural network (B-CNN) to predict photo-$z$ along with the PDFs from fluxes and images, respectively. We combine the B-MLP and B-CNN together, and construct a hybrid network and employ the transfer learning techniques to investigate the improvement of including both flux and image data. For galaxy samples with SNR$>$10 in $g$ or $i$ band, we find the accuracy and outlier fraction of photo-$z$ can achieve $σ_{\rm NMAD}=0.022$ and $η=2.35\%$ for the B-MLP using flux data only, and $σ_{\rm NMAD}=0.022$ and $η=1.32\%$ for the B-CNN using image data only. The Bayesian hybrid network can achieve $σ_{\rm NMAD}=0.021$ and $η=1.23\%$, and utilizing transfer learning technique can improve results to $σ_{\rm NMAD}=0.019$ and $η=1.17\%$, which can provide the most confident predictions with the lowest average uncertainty.
