题 目： Optimization of Tessellation-based Statistics: Void Statistics
报 告 人：刘雨
时 间：2024年12月24日 9:00
地 点：理科楼LE201
邀 请 人：周思益
报告摘要： Galaxy survey provides an important window for the exploration of fundamental physics by accurately mapping the large-scale structure of the Universe (LSS), which is expected to lead to great scientific discoveries. In cosmology, LSS encodes abundant key cosmological information (e.g., baryon acoustic oscillations, neutrino masses, primordial non-Gaussianities, gravity properties, and cosmological parameters). How to efficiently extract these valuable information has been an important research topic in galaxy survey science. In order to meet the scientific needs of new-generation surveys, we are developing and optimizing a variety of important cosmological techniques (i.e., non-Gaussian statistics, initial condition reconstruction, and cosmological N-body simulation) by solving several key and urgent problems in this field. In this talk, I will give a introduction to our recent work progresses on this topic (mainly focusing on Void Statistics). These works are expected to play an important role in the processing and analysis of future galaxy survey data, and ultimately bring more scientific returns.
报告人介绍： Dr. Yu Liu is currently a postdoctoral fellow (MUST & Shui Mu fellow) at the Astronomy Department of Tsinghua University. Yu Liu obtained his PhD degree in astrophysics from Shanghai Jiao Tong University in June 2022. His research direction is the cosmic large-scale structure (LSS) in cosmology, and his research interests mainly focus on non-Gaussian statistics, initial condition reconstruction, cosmological N-body simulation, neutrino cosmology, and baryon acoustic oscillations (BAO).
