Title: Cosmology in the non-linear regime: two novel methods
Institute: School of Physics and Astronomy, Sun Yat-Sen University
Host: Haoran Yu
Time: 14:30-16:30, Thursday, 2021-3-25
Location: Physics Building 552
The stage-IV large-scale galaxy survey experiments will measure many cosmological parameters with unprecedented accuracy, opening a new era of accurate cosmology. This also brings great challenges to the statistical analysis of survey data. To extract more cosmological information from the data, we developed several new statistical analysis methods for the survey data, which can go deep into the non-linear clustering scale and obtain competitive cosmological results. In this talk I will introduce two methods: 1) The tomographic Alcock-Paczynski method, which cleverly distinguished the AP effect from the red shift distortion via the redshift evolution. When applied to the BOSS observation data, the accuracy of dark energy, Hubble constant and neutrino parameters were improved by 20-50%. This is equivalent to extracting nearly twice as much cosmological information from the data. 2) The artificial intelligence (AI) method, which can measure cosmological parameters in non-linear scale. Its probing precision is 10 times better than that of two-point correlation statistics. These methods can be used as an important supplement to the traditional analysis methods, to obtain more cosmological information from the future galaxy survey experiments.
2003.9 – 2007.7 B. S. Student, Department of Modern Physics, University of Science and Technology of China (USTC).
2007.9 – 2012.7 Ph.D. Candidate, Department of Modern Physics, University of Science and Technology of China (USTC).
2013.3 – 2017.3 Research Fellow, Korea Institute for Advanced Study (KIAS)
2017.9 – now Associate Professor, School of Physics and Astronomy, Sun Yat-Sen University (SYSU)
Research Interests: Large Scale Structure of the Universe Galaxy Surveys, Dark Energy, Dark Matter, CMB Physics