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Searching for strong lenses in Kilo Degree survey with Convolutional Neural Networks
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)
报告时间:2020-11-03 15:00:00
报告地点:天文楼402会议室

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 questionsHowever, 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.