Distributed asteroid discovery system for large astronomical data

Thanks to the advanced technology in astronomical observations, astronomers have collected massive data sets. One of the research tasks is to discover asteroids based on observational data. However, with the fast-growing volume of astronomical data sets, this task becomes enormously relied on computing power. Therefore, the cloud-enable distributed computation and super powerful computation power can offer a good solution to this field. We adopt the Hough transform to link the sequential DETECTIONs of asteroids, and also design a distributed algorithm to process this task on distributed cloud environment for flexible and efficient calculation. Our designs are developed on MapReduce and Spark frameworks. We also utilizes HDFS and HBase in order to reduce disk I/O overhead and increase reliability and scalability. The system architecture is built on the OpenStack which allocates hardware resources flexibly. Our experiment results show significant improvements of calculation time. We believe this will help astronomers to interactively access and analyze data to discover asteroids. The system can also incrementally update DETECTIONs linking with new observed data. It also provides the visual interface to inspect the linked DETECTIONs of asteroids.