9 citations · 15 across the 6 of their papers we have counts for
6 papers
A New Wavelet Scattering Transform-Based Statistic for Cosmological Analysis of Large-Scale Structure
Zhujun Jiang, Xiaolin Luo, Wenying Du +6
Large-scale structure (LSS) analysis in galaxy surveys is a powerful cosmological probe but is limited by tracer bias, which can obscure underlying information and weaken parameter…
AI-Driven Reconstruction of Large-Scale Structure from Combined Photometric and Spectroscopic Surveys
Wenying Du, Xiaolin Luo, Zhujun Jiang +7
Galaxy surveys are crucial for studying large-scale structure (LSS) and cosmology, yet they face limitations--imaging surveys provide extensive sky coverage but suffer from photo-$…
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…
Improving Constraint on from SDSS Using Marked Correlation Functions
L. M. Lai, J. C. Ding, X. L. Luo +9
Large-scale structure (LSS) surveys will increasingly provide stringent constraints on our cosmological models. Recently, the density-marked correlation function (MCF) has been int…
Fast generation of mock galaxy catalogues with COLA
Jiacheng Ding, Shaohong Li, Yi Zheng +3
We investigate the feasibility of using COmoving Lagrangian Acceleration (COLA) technique to efficiently generate galaxy mock catalogues that can accurately reproduce the statistic…