20 citations · 35 across the 6 of their papers we have counts for
6 papers
Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation
Zitong Wang, Feng Shi, Le Zhang +5
Twenty-one-centimeter intensity mapping is a powerful probe of the large-scale distribution of neutral hydrogen (HI) and cosmological observables such as baryon acoustic oscillatio…
Extreme Solar Storm Reveals Causal Interactions in Space Weather
Xinan Dai, Haiyang Fu, Zichong Yan +5
Solar storms perturb Earth's magnetosphere, triggering geomagnetic storms that threaten space-based systems and infrastructure. Despite advances in spaceborne and ground-based obse…
Cosmological distance forecasts for the CSST Galaxy Survey using BAO peaks
Feng Shi, Jieyi Tian, Zhejie Ding +21
The measurement of cosmological distances using baryon acoustic oscillations (BAO) is crucial for studying the universe's expansion. The Chinese Space Station Telescope (CSST) gala…
DarkAI: Reconstructing the density, velocity and tidal field of dark matter from a DESI-like bright galaxy sample
Feng Shi, Zitong Wang, Xiaohu Yang +10
Reconstructing the mass density, velocity, and tidal (MTV) fields of dark matter from galaxy surveys is essential for advancing our understanding of the LSS of the Universe. In thi…
21-cm foreground removal using AI and frequency-difference technique
Feng Shi, Haoxiang Chang, Le Zhang +5
The deep learning technique has been employed in removing foreground contaminants from 21 cm intensity mapping, but its effectiveness is limited by the large dynamic range of the f…
(DarkAI) Mapping the large-scale density field of dark matter using artificial intelligence
Zitong Wang, Feng Shi, Xiaohu Yang +3
Herein, we present a deep-learning technique for reconstructing the dark-matter density field from the redshift-space distribution of dark-matter halos. We built a UNet-architectur…