Topic : Cosmological constraints from the large-scale structure

Speaker: Xiao-Dong Li, Sun Yat-sen University

Time: 14:00, October 17, 2022

Venue: online

Tencent meeting ID: 557-516-949

Link: https://meeting.tencent.com/dm/3U71zGzWICAf

Abstract: The stage-IV large scale surveys will probe cosmological parameters with unprecedented accuracy. The analysis of the survey data will bring great challenges to us. Therefore, we are working on several methods, which can enter the non-linear clustering scale to extract cosmological information, and obtain competitive cosmological results. 1) The "tomographic Alcock-Paczynski" method, which distinguishes the AP distortion from the redshift space distortions by focusing on the redshfit evolution of the anisotropy. 2) The mark weighted statistics, which can significantly improve the constraints compared with the standard two-point statistics. 3) The machine learning methods, which have great potential in constraining cosmological parameters and reconstructing the cosmic velocity field. The above methods are complimentary to the traditional methods, and is expected to play important roles in the analysis of the future surveys. In the next several yeas, the focus of our work is to prepare for the application of these methods in the CSST survey.

Biography: 李霄栋，中山大学物理与天文学院副教授。长年从事宇宙学领域相关研究，内容涉及宇宙大尺度结构、巡天数据分析、暗能量问题理论与观测、引力波等。为解决非线性成团尺度巡天数据分析的困难问题，与合作者提出了名为“切片Alcock-Paczynski方法”的宇宙学分析方法，在SDSS观测数据分析获得当时世界最强的暗能量限制，获得众多国内外专家高度评价。提出了构建宇宙网的beta-网络方法，发展了marked统计用于宇宙学限制。近期将将人工智能技术应用于宇宙网结构构造、宇宙学参数限制、速度场重构。目前已发表论文近50篇，获得引用超过2000次。