网站地图 联系我们 English 中国科学院
首页紫台简介机构设置新闻动态科研成果研究队伍合作交流天文学院创新文化党群园地信息公开
新闻动态
图片新闻
综合新闻
天文快讯
Colloquium & 学术交流
国内外天文学术会议
紫台通讯
传媒扫描
科普动态
科研信息
台内新闻
您当前的位置:首页>新闻动态>Colloquium & 学术交流
6月19日 From image processing to modeling astrophysical systematics: the potential of Deep Learning for modern surveys
2018年06月19日

  报告题目: From image processing to modeling astrophysical systematics: the potential of Deep Learning for modern surveys

  报告人:Dr. Francois Lanusse

  Abstract:The upcoming generation of cosmological surveys such as LSST will aim to shed some much needed light on the physical nature of dark energy and dark matter by mapping the Universe in great detail and on an unprecedented scale. While this implies a great potential for discoveries, it also involves new and outstanding challenges at every step of the science analysis, from image processing to the modeling of astrophysical systematics.

  In this talk I will illustrate how recent advances in Deep Learning open new perspectives for addressing some of theses challenges and for exploiting this wealth of data in new and exciting ways. As a first example, I will present our work on automated strong gravitational lens detection, a problem made tractable at the scale of LSST by Deep Learning by essentially eliminating the need for human visual inspection. In a second example of applications, I will illustrate how data-driven deep generative models can be used to complement a physical modeling in two different situations: image simulations with realistic galaxy morphologies for the calibration of weak lensing shape measurement algorithms, and the production of mock galaxy catalogs with realistic intrinsic alignments learned from hydrodynamical simulations.

  时间:6月19日(星期二)09:30

  地点:紫台5-216会议室

欢迎大家参加!

                                                      紫金山天文台学术委员会

地址:(210034)南京市栖霞区元化路8号(南大科学园内) 电话:86-25-83332000 传真:86-25-83332091
版权所有:中国科学院紫金山天文台 http://www.pmo.cas.cn pmoo@pmo.ac.cn 备案序号:苏ICP备05007736号