The application of machine learning to astronomical imaging surveys

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分类物体。随着LSST 10年调查的开始，只有4年的时间，需要有强大而可靠的工具来选择最稀有的物体，例如遥远的类星体，低质量恒星，丰富的星系团，是迫切的。使用UKIDSS/PanSTARRS和VIKING/KiDS匹配的近红外和光学调查作为训练集，这些技术将得到优化。重点将是利用光度目录来解决从低质量恒星到最遥远的类星体的广泛科学目标。

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.