activity
20182024
most citedCosmological Constraint Precision of the Photometric and Spectroscopic Multi-probe Surveys of China Space Station Telescope (CSST)

43 citations · 130 across the 10 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…