I will introduce several new LSS analysis methods that we are working on. 1) The tomographic Alcock-Paczynski method, which overcomes the RSDs via looking at the redshift evolution, and is capable of extracting abundant information on 6-40 Mpc/h clustering scales. 2) The convolution neural network (CNN) technique, which allows we to infer the underlying cosmology information directly from the 3D matter density distribution. 3) The beta-skeleton method as a new tool for cosmic web analysis. I will introduce their concepts, the results we have obtained, as well as our plans for their application to the CSST data.
BIO
In 2012, Xiao-Dong Li got his PhD in USTC. He then went to the Korea Institute for Advance Study (KIAS) as a postdoc. There he spent four years working with Prof. Changbom Park, developing a new method doing the AP (Alcock-Paczynski) test, and applied it to the SDSS data. In 2017, he joined Sun Yat-Sen University as an associate professor.
Host: Dandan Xu
