preloader

Cosmology and structure formation


Cosmology is a branch of science aiming to probe the origin and evolution of the Universe and the structures therein. The tremendous developments, both observational and theoretical, in the past decades have revolutionized our understandings about the cosmos. Meanwhile, new challenges have emerged. What is the nature of dark matter and dark energy? Is General Relativity still the governing theory on cosmological scales? These are the fundamental questions to be answered in the 21st century. Galaxies and clusters of galaxies are observable tracers of large-scale structures in the Universe. Understanding their formation and evolution is essentially important in astrophysical and cosmological studies. Targeting at answering these fundamental questions, our studies employ multiple probes and tracers for cosmological analyses.


宇宙学宇宙学2


Regarding gravitational lensing, cosmic microwave background radiation, galaxies and clusters of galaxies, we investigate the impacts of dark matter and dark energy on the formation and evolution of large-scale structures through theoretical investigations, numerical simulations, and observational analyses. Developing new statistical tools for cosmological studies and understanding possible systematics that could affect their applicability are also our main efforts. Observational data from international communities have been extensively used in our research. We also actively involve in different domestic projects.


宇宙学3

With the observational developments, especially with the construction of the SKA project,21cm cosmology will enter its golden age. Arising from the hyperfine energy structure of hydrogen atoms, the characteristics of 21cm signals are sensitive to the birth of the first generation of structures and the nature of the matter components in the Universe. Being heavily involved in SKA, we aim to investigate in depth the potential of extracting cosmological information from 21cm observations by exploring different statistical methods. We also employ machine-learning techniques in our studies.