4 papers
Recovering Cosmic Structure with a Simple Physical Constraint
Tian-Cheng Luan, Xin Wang, Jiacheng Ding +3
Radio observation of the large-scale structure (LSS) of our Universe faces major challenges from foreground contamination, which is many orders of magnitude stronger than the cosmi…
Restoring Missing Modes of 21cm Intensity Mapping with Deep Learning: Impact on BAO Reconstruction
Qian Li, Xin Wang, Xiaodong Li +3
In 21cm intensity mapping of the large-scale structure (LSS), regions in Fourier space could be compromised by foreground contamination. In interferometric observations, this conta…
AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution
Xu Xiao, Jiacheng Ding, XiaoLin Luo +6
We propose a UNet-based deep learning model to reconstruct the real-space dark matter (DM) velocity field from the redshift-space distribution of sparse DM halos. Using various sta…
Cosmological constraints from the density gradient weighted correlation function
Xiaoyuan Xiao, Yizhao Yang, Xiaolin Luo +8
The mark weighted correlation function (MCF) is a computationally efficient statistical measure which can probe clustering information beyond that of the conventional 2-po…