【学术报告】
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报告题目： Galaxy Structure and Dark Matter content using Machine Learning.Paving the road to CSST
报告人： Prof. Nicola R. Napolitano
报告人单位: 中山大学
报告时间： 2021年7月20日（星期二）下午 3:00-4:00
报告地点： 主楼328会议室
报告语言: 英文
报告摘要：
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Large sky survey are a golden mine to study galaxy structure and dark matter content. With current ground-based galaxies we can study the structural parameters of millions of galaxies and search for gravitational lenses among them to study their dark matter content. In this way we can reconstruct the evolution of dark matter fractions of massive elliptical galaxies up to ~6 Gyr back in time. With new space telescopes, in particular the Chinese Space Station Telescope (CSST), thanks to the higher sensitivity and image quality, we will expand these investigations even to higher look back times, up to ~10Gyr, on a wider parameter space, including <L* galaxies. We will have the chance to observe and we will need to measure structural parameters of billions of galaxies and find hundreds of thousand strong lensing events. To deal with this huge amount of data we will need to use deep learning techniques. I will summarise the current status of the machine learning tools we are developing at SYSU and the recent strong lensing findings, including a new class of Einstein crosses from ultra-compact high-redshift galaxies. I will also discuss the plans for the application of ML techniques to CSST science.
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云台邀请团组：高能天体物理组
邀请方联系人：王建成 (13608718888)
7月17日
机构概况
