Real-Time Query Enabled By Variable Precision In Astronomy

As sky survey projects coming out, petabytes and exabytes of astronomical data are continuously collected from highly productive space missions. Especially, in time-domain astronomy, Short-Timescale and Large Field-of-view (STLF) sky survey not only requires real-time analysis on short-time data, but also need precise astronomical data for special phenomena. Additionally, it is important to find a partition method and build an index based on that for effective storage and query. However, the existing methods cannot simultaneously support real-time and variable-precision query in astronomy. In this paper, we propose a novel astronomical real-time and variable precision query method based on data partitioning with Hierarchical Equal Area isoLatitude Pixelation (HEALPix for short). Our method calculates the time through model and predict precision by machine learning, which can accurately predict the partition level number of HEALPix which can effectively reduce the cost of time for query by layer and layer. The method can meet the user's requirements of real-time and variable-precision query. The experimental results show that our method can optimize previous query strategies and reach a better performance.