activity
20182024
most citedCan Non-standard Recombination Resolve the Hubble Tension?

56 citations · 247 across the 16 of their papers we have counts for

collaborators

16 papers

astro-ph.CO2024

Cosmological Prediction of the CSST Ultra Deep Field Type Ia Supernova Photometric Survey

Minglin Wang, Yan Gong, Furen Deng +3

Type Ia supernova (SN Ia) as a standard candle is an ideal tool to measure cosmic distance and expansion history of the Universe. Here we investigate the SN Ia photometric measurem…

astro-ph.CO2023

Forecasting the BAO Measurements of the CSST galaxy and AGN Spectroscopic Surveys

Haitao Miao, Yan Gong, Xuelei Chen +3

The spectroscopic survey of China's Space Survey Telescope (CSST) is expected to obtain a huge number of slitless spectra, including more than one hundred million galaxy spectra an…

astro-ph.CO2022★ 43 cited

Cosmological Constraint Precision of the Photometric and Spectroscopic Multi-probe Surveys of China Space Station Telescope (CSST)

Haitao Miao, Yan Gong, Xuelei Chen +3

As one of Stage IV space-based telescopes, China Space Station Telescope (CSST) can perform photometric and spectroscopic surveys simultaneously to efficiently explore the Universe…

astro-ph.CO2022

Cosmological constraints from the density gradient weighted correlation function

Xiaoyuan Xiao, Yizhao Yang, Xiaolin Luo +8

The mark weighted correlation function (MCF) is a computationally efficient statistical measure which can probe clustering information beyond that of the conventional 2-po…

astro-ph.CO2021★ 6 cited

Self-calibrating interloper bias in spectroscopic galaxy clustering surveys

Yan Gong, Haitao Miao, Pengjie Zhang +1

Contamination of interloper galaxies due to misidentified emission lines can be a big issue in the spectroscopic galaxy clustering surveys, especially in future high-precision obse…

astro-ph.CO2021★ 22 cited

Cosmic Velocity Field Reconstruction Using AI

Ziyong Wu, Zhenyu Zhang, Shuyang Pan +6

We develop a deep learning technique to infer the non-linear velocity field from the dark matter density field. The deep learning architecture we use is an "U-net" style convolutio…

astro-ph.CO2020★ 10 cited

Using the Mark Weighted Correlation Functions to Improve the Constraints on Cosmological Parameters

Yizhao Yang, Haitao Miao, Qinglin Ma +8

We used the mark weighted correlation functions (MCFs), , to study the large scale structure of the Universe. We studied five types of MCFs with the weighting scheme , w…

astro-ph.CO2020★ 21 cited

Update constraints on neutrino mass and mass hierarchy in light of dark energy models

Zhenjie Liu, Haitao Miao

Combining cosmic microwave background (CMB) data from Planck satellite data, Baryon Acoustic Oscillations (BAO) measurements and Type Ia supernovae (SNe Ia) data, we obtain the bou…

astro-ph.CO2019★ 56 cited

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…

astro-ph.CO2019★ 5 cited

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…

astro-ph.CO2019★ 1 cited

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.…

astro-ph.CO2019★ 14 cited

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…

astro-ph.CO2019★ 28 cited

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…

astro-ph.CO2018★ 7 cited

Flatness without CMB - the Entanglement of Spatial Curvature and Dark Energy Equation of State

Haoting Xu, Zhiqi Huang, Zhenjie Liu +1

The cosmic spatial curvature parameter is constrained, primarily by cosmic microwave background (CMB) data, to be very small. Observations of the cosmic distance ladder and t…

gr-qc2018★ 3 cited

Anti-Evaporation/Evaporation of -dimensional Reissner-Nordström Black Hole

YuHong Fang, Zhiqi Huang, HaiTao Miao +1

We generalize -theory of anti-evaporation/evaporation for a Reissner-Nordström black hole in -dimensional space-time. We consider non-linear conformally invariant Maxwell…

astro-ph.CO2018★ 31 cited

The Tension in Non-flat QCDM Cosmology

Haitao Miao, Zhiqi Huang

The recent local measurement of Hubble constant leads to a more than tension with Planck + CDM (Riess {\it et al} 2018). In this article we study the tension in non-f…