56 citations · 247 across the 16 of their papers we have counts for
5 papers · 2 filters
Can Non-standard Recombination Resolve the Hubble Tension?
Miaoxin Liu, Zhiqi Huang, Xiaolin Luo +3
The inconsistent Hubble constant values derived from cosmic microwave background (CMB) observations and from local distance-ladder measurements may suggest new physics beyond the s…
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…
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.…
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…