實驗結果顯示，以本論文提出的方法所偵測到的小行星，在偵測準確率的部份，在10組隨機生成的資料集中找到了90%的小行星，且移動速度在每秒10-2像素的量級下相當精準；在偵測效率的部分，對圖片位移與疊合的操作在圖形處理器的幫助下，節省了88%的運算時間，而FIND演算法的運算在Apache Spark的幫助下，平均可節省81%的時間；在實際的流程裡，分散式架構和圖形處理器平均分別可以節省64%和37%的總體運算時間。總結來說，透過分散式的平行運算和圖形處理器，此方法在保有一定的偵測準確率的條件下，最多可以成功地節省77%的運算時間，能夠為天文學家在小行星偵測的工作上達到更佳的效率。 ;Studies of asteroids have become more and more important since they are believed to help astronomical scientists to know the early evolution of the solar system. While the number of asteroids detected has grown numerously in recent years due to the significant improvement of photometry techniques, the detection of faint asteroids are still hard to achieve. In 2014, a method called Synthetic Tracking was brought up, which greatly enhances the performance of faint asteroids detection. Since it requires large-scale of workloads and costs plenty of time, we propose a new computing architecture to increase the performance.
Combining Synthetic Tracking with FIND algorithm in DAOPHOT software tool, our architecture shift-and-add simulated astronomical images to enhance signals of faint asteroids and detect their accurate velocities and locations. The system is established on distributed platform with GPU equipped, which largely speed up the performance of image processing.
Result shows that the accuracy of velocity detected is very high with 10-2 pixel per frame; the error of location is 2 pixels in average. For performance, the shift-and-add process saves 88% of time with GPU; the FIND algorithm saves 81% of time with help of Apache Spark. The total performance is enhanced 77% in average.
