Data Mining for Gravitational Lenses and Interacting Galaxies

• Main Authors: Sze-Yeong Tan, 陳詩湧
• Other Authors: Wing-Huen Ip
• Format: Others
• Language: en_US
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/05382460875998395002

碩士 === 國立中央大學 === 天文研究所 === 92 === The scientific operations of space telescopes and ground-based facilities worldwide have produced a flood of astronomical data waiting to be analyzed. Thus the development of fast and efficient system is in urgent demand for the purpose of data mining. The discovery of gravitational lensing events and interacting galaxies are very important in the study of cosmology. However, both types of structures are relatively rare and often hidden in the mountain of images. For these reasons, we have developed an automatic system to identify these objects from image archives by shape analysis. First, candidates are selected with the shape parameter defined by our method and a line and an arc are then fitted to these potential candidates. From error analysis the best shape can be identified. The algorithm developed in this work has been tested on two of the gravitational lensing events found in the RCS and proved to be successful. Furthermore, it has also been applied to a portion of the RCS data set, which consists of 210 images and dozens of interacting galaxies have been found.