新利18体育新

首页 Home | 新利体育客户端网址 | 人员 People | 科学研究 Science | 科研设备 Facilities | 新利18最新体育娱乐 | 人才培养 Education | 国际合作 Collaborations | 科学普及 Outreach | 诚聘英才 Recruitment | 访问指南 For Visitors
首页 Home
新利体育客户端网址
人员 People
科学研究 Science
新利18最新体育娱乐
人才培养 Education
科学普及 Outreach
诚聘英才 Recruitment
访问指南 For Visitors
内部链接 Internal
过往活动 Past Events
当前位置:首页 Home>>过往活动 Past Events>>正文

Lunch talk on Mar. 7, 2022

Photometric Objects around Cosmic Webs (PAC): Taking Full Use of the Spectroscopic and Photometric Surveys


Speaker:Kun Xu (SJTU)

Venue:Video Conference

Time:12:30 PM, Monday, Mar. 7, 2022

Abstract:

In recent years, upcoming large and deep spectroscopic and photometric cosmological surveys (e.g., DESI, LSST, Euclid, CSST, PFS) will uncover the universe to an unprecedented width and depth. Spectroscopic and photometric surveys both have their advantages and disadvantages. Spectroscopic surveys can precisely measure the 3D positions while photometric surveys are deeper and more complete. Thus, methods and techniques which can combine and take full use of both these two surveys will bring much more information for galaxy formation, galaxy-halo connection and cosmology studies. I’d like to introduce our new method PAC that can estimate the projected density distribution of photometric objects with certain physical properties (e.g., luminosity, mass, color etc.) around spectroscopic objects. As an example, we apply the PAC method to the CMASS spectroscopic and HSC-SSP PDR2 photometric samples. We can accurately measure the stellar-halo mass relation (SHMR), stellar mass function (SMF) and conditional stellar mass function (CSMF) for a wide stellar mass range ($10^{9.0}-10^{12.0} M_{\odot}$) at redshift $z~0.6$. We also find morphology, color and galaxy size dependences of the SHMR, which may indicate the existence of so called galaxy assembly bias. PAC can have many other applications in galaxy formation and cosmology with the large and deep surveys.


Report PPT:SWIFAR_Kun Xu.pptx



附件【SWIFAR_Kun Xu.pptx已下载
关闭窗口

版权所有:新利18体育新

新利体育平台怎么样

Baidu
map