报告题目：Searching for strong lenses in Kilo Degree survey with Convolutional Neural Networks

　　报告人：李瑞（中山大学，博后）

　　报告人简介：李瑞，毕业于中科院云南天文台，现为中山大学物理天文学院博士后，主要研究方向为星系尺度的强引力透镜。现在主要致力于利用机器学习搜索强引力透镜候选体并利用证认后的透镜系统研究星系的演化以及星系中的暗物质等。

　　摘要：Strong lensing (SL) is the effect of the deformation of light of background galaxies due to the gravitational potential of intervening systems which act as lenses or “deflectors” (usually galaxies or galaxy groups/clusters). This effect, predicted by general relativity, manifests itself with the creation of spectacular arcs or multiple point images around the deflectors. SLs can measure the mass of the deflectors with much high accuracy than any of other methods, making it particularly suitable for studying a large number of astrophysical and cosmological open questions. However, at present, the known and confirmed SLs are just a few hundreds, far from enough for probing the scientific topics mentioned above with large statistical samples. Now, we have a great opportunity to enlarged the SL sample. The ongoing (e.g. Kilo Degree Survey, KiDS; Dark Energy survey, DES; Hyper Suprime-Cam, HSC;) and the next generation sky surveys (e.g., Large Synoptic Survey Telescope, LSST; Euclid; Chinese Space Station Telescope, CSST) provide us large database of galaxies (Millions to Billions) from which we expect to find a great number of SLs (~10^5). We are working on searching SLs in sky surveys with Machine Learnig. In this talk, I will introduce the new progress we have made in the lens searching work. I will also talk about two of our new findings: 1.first discovery of post-blue nugget galaxies through strong lensing. 2. Two strong lenses in one cluster.

　　报告时间：2020年11月4日（周三）14:00

　　报告地点：紫台5-516会议室

欢迎大家参加！

紫金山天文台学术委员会