星系合併是星系演化最重要的關鍵。但我們並沒有足够大量且均勻的遙遠樣本來研究在久遠的過去所發生的星系演化過程。我們在過去一直嚐試研發新的天文影像型態辯識程式以搜索天空中遙遠的小型合併星系與星系團。過去二年，我們在尋找合併星系的研究方面有了重大的突破。我們利用我們初步發展的影像辯識程式來尋找合併星系，在400 度的天空範圍內，找到了約一萬五千個合併星系。這是目前為止世上最大的合併星系目錄，同時也是最均勻的遙遠星系合併及演化樣本。使用此目錄，我們另外找到了約九個新的星系團。雖然此程式是目前世界上最有效率的合併星系辨識程式，但是其效率仍然不佳，且無法替這些星系做光度量測。本計晝的目的是要改善我們已有的影像型態辯識程式，並針對合併星系發展適當的測光程式。依我們目前發展中的一些新程式的實驗顯示，新的程式對合併星系的辨識效率可以增加十倍以上，預期我們將可以獲得一個超過十萬個合併星系的新目錄，站穩我們在該領域的世界領先地位。另外我們將發展能處理任意型態光源的孔徑測光程式，這也將是全世界最先進的天體自適孔徑測光方法。我們也將利用這些方法所得出的大量結果，來研究星系演化及其與星系團的關係。Galaxy merging is the most important process in galaxy evolution. However, people do not have enough uniform samples of distant faint merging galaxies to investigate how galaxies were evolving. For the past few years, we have been developing new algorithms in order to search for distant faint merging galaxies and galaxy clusters. We had an important breakthrough in searching merging galaxies last year. We developed a new pattern recognition algorithm to identify merging galaxies. We discovered ~ 15,000 new pairs of merging galaxies within 422 degree square of Red Sequence Cluster Survey 2 (RCS2) of CFHT observations. Furthermore, we also found nine new candidates of galaxy clusters by searching for regions with significant density enhancements of merging galaxies. This catalog has the largest number of morphologically identified interacting and merging galaxies based on consistent searching criteria. These sources provide a uniform sample of merging galaxies for further photometric and spectroscopic studies of galaxy evolution. Although the pattern-recognition algorithms are currently the best algorithms for searching merging galaxies, it is still not efficient enough. Furthermore, we cannot perform photometric measurements in the current algorithms. In this project, we plan to improve the algorithms to make it more efficient by adding some more shape parameters and to develop new photometric algorithms suitable for merging galaxies. In some preliminary testing, we found that we would be able to improve the efficiency by a factor of ten with proper shape parameters and moment analyses. We thus expect to discover more than 100,000 merging galaxies even just with our current available RSC2 data sets. In the photometric measurements of merging galaxies, tradition apertures are not very suitable because the shapes of the merging galaxies are usually very complicated. We will also develop an adaptive algorithm of aperture selections for photometric measurements based on our pattern recognition methods. The aperture algorithm will be useful for merging galaxies of irregular patterns and for sources in a crowded region. We will study galaxy evolution and search for new galaxy clusters using the results obtained from our searching and photometric algorithms. The huge amounts of data and information we can extract from our new techniques are expected to have big impacts on the researches of galaxy evolution and large-scale structures of the universe. 研究期間：10008 ~ 10107
