812 citations · 2.7k across the 46 of their papers we have counts for
7 papers · 1 filter
Cosmological constraints from the redshift dependence of the Alcock-Paczynski effect: Fourier space analysis
Xiaolin Luo, Ziyong Wu, Xiao-Dong Li +3
The tomographic Alcock-Paczynski (AP) method utilizes the redshift evolution of the AP distortion to place constraints on cosmological parameters. It has proved to be a robust meth…
Cosmological parameter estimation from large-scale structure deep learning
Shuyang Pan, Miaoxin Liu, Jaime Forero-Romero +4
We propose a light-weight deep convolutional neural network (CNN) to estimate the cosmological parameters from simulated 3-dimensional dark matter distributions with high accuracy.…
Alcock-Paczynski Test with the Evolution of Redshift-Space Galaxy Clustering Anisotropy
Hyunbae Park, Changbom Park, Cristiano G. Sabiu +5
We develop an improved Alcock-Paczynski (AP) test method that uses the redshift-space two-point correlation function (2pCF) of galaxies. Cosmological constraints can be obtained by…
The redshift dependence of Alcock-Paczynski effect: cosmological constraints from the current and next generation observations
Xiao-Dong Li, Haitao Miao, Xin Wang +5
The tomographic Alcock-Paczynski (AP) test is a robust large-scale structure (LSS) measurement that receives little contamination from the redshift space distortion (RSD). It has p…
Non-parametric dark energy reconstruction using the tomographic Alcock-Paczynski test
Zhenyu Zhang, Gan Gu, Xiaoma Wang +7
The tomographic Alcock-Paczynski (AP) method can result in tight cosmological constraints by using small and intermediate clustering scales of the large scale structure (LSS) of th…
Graph Database Solution for Higher Order Spatial Statistics in the Era of Big Data
Cristiano G. Sabiu, Ben Hoyle, Juhan Kim +1
We present an algorithm for the fast computation of the general -point spatial correlation functions of any discrete point set embedded within an Euclidean space of $\mathbb{R}^…