5 citations · 9 across the 5 of their papers we have counts for
5 papers
Enhancing Cosmological Constraints by Two-dimensional -cosmic-web Weighted Angular Correlation Functions
Fenfen Yin, Liang Xiao, Wenying Du +6
In this study, we investigate the potential of mark-weighted angular correlation functions (MACFs), which integrate -cosmic-web classification with angular correlation function…
Tomographic Alcock-Paczynski Test with Marked Correlation Functions
Liang Xiao, Limin Lai, Zhujun Jiang +2
The tomographic Alcock-Paczynski(AP) method, developed over the past decade, exploits redshift evolution for cosmological determination, aiming to mitigate contamination from redsh…
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…
Deep learning for cosmological parameter inference from a dark matter halo density field
Zhiwei Min, Xu Xiao, Jiacheng Ding +11
We propose a lightweight deep convolutional neural network (lCNN) to estimate cosmological parameters from simulated three-dimensional dark matter (DM) halo distributions and assoc…
Improving SDSS cosmological constraints through -skeleton weighted correlation functions
Fenfen Yin, Jiacheng Ding, Limin Lai +6
The -skeleton approach can be conveniently utilized to construct the cosmic web based on the spatial geometry distribution of galaxies, particularly in sparse samples. This meth…