5 citations · 10 across the 7 of their papers we have counts for
4 papers · 1 filter
Closing the Observational Gap in Cosmic Dynamics: AI-Enabled Reconstruction of the Universe's Vorticity and Rotational Flow Morphology
Ziyong Wu, Xu Xiao, Fuyu Dong +7
The cosmic vorticity field, an essential tracer of nonlinear structure formation, has remained observationally inaccessible because transverse galaxy motions are difficult to measu…
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…
Cosmological constraints from the redshift dependence of the Alcock-Paczynski effect: Possibility of estimateing the non-linear systematics using fast simulations
Qinglin Ma, Yiqing Guo, Xiao-Dong Li +5
The tomographic AP method is so far the best method in separating the Alcock-Paczynski (AP) signal from the redshift space distortion (RSD) effects and deriving powerful constraint…