Abstract: The large-scale structure contains a vast amount of information about the dark energy, modified gravity, and early universe physics, etc. Particularly, next-generation intensity mapping (e. g. SKA, CHIME, Tianlai, HIRAX) will be capable of observing the high redshift density fluctuation more efficiently. The challenges, however, are also evident as both theoretical and instrumental systematics are becoming more significant than statistical error, which could then reduce the measurement accuracy of cosmological parameters. In this talk, I will discuss various limiting factors including theoretical uncertainties, non-linear information loss, clustering bias, and non-Gaussian covariance matrix, and then demonstrate strategies to maximize the information content for future large-scale structure surveys. 

   After getting his Ph.D. from National Astronomical Observatories, CAS in 2011, Xin became an Assistant Research Scientist at Johns Hopkins University. From 2015, he is a postdoctoral fellow at Canadian Institute for Theoretical Astrophysics. His research interests lie generally in cosmology, particularly in understanding the evolution of our Universe at different scales and epochs. More specifically, he mainly focuses on the optimal extraction of cosmological information from surveys, the theory of structure formation, dark energy, and modified gravity, etc.