The application of machine learning to astronomical imaging surveys

点击翻译按钮获取中文摘要

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.