The application of machine learning to astronomical imaging surveys

机器学习在天文成像测量中的应用

• 批准号: 1944971
• 金额: --
• 依托单位: Durham University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1944971
• 关键词: application; machine; learning; astronomical; imaging

The aim of this project is to apply a range of machine learning and data mining algorithms to aid the classification of objects from current and future astronomical imaging surveys. With the start of the LSST 10 year survey only 4 years away, the need to have robust and reliable tools to select the rarest objects, e.g. distant quasars, low mass stars, rich clusters of galaxes, is pressing. Using the UKIDSS/PanSTARRS and VIKING/KiDS matched NIR and optical surveys as a training set, these techniques will be optimised. The emphasis will be on exploiting the photometric catalogues to address a wide range of science goals from low mass stars to the most distant quasars.

该项目的目的是应用一系列机器学习和数据挖掘算法，以帮助从当前和未来的天文成像调查中对物体进行分类。距离LSST 10年观测的开始只有4年了，迫切需要有强大可靠的工具来选择最稀有的天体，例如遥远的类星体、低质量恒星、丰富的星系团。使用UKIDSS/PanSTARRS和Viking/KILDS匹配的近红外和光学测量作为训练集，这些技术将得到优化。重点将放在利用光度学星表来解决从低质量恒星到最远类星体的广泛科学目标。