AstroCatR: a mechanism and tool for efficient time series reconstruction of large-scale astronomical catalogues. Issue 1 (23rd May 2020)

• Record Type: Journal Article
• Authors: Yu, Ce; Li, Kun; Tang, Shanjiang; Sun, Chao; Ma, Bin; Zhao, Qing
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 496:Issue 1(2020)
• Page Start: 629
• Page End: 637
• Publication Date: 2020-05-23
• Subjects: methods: data analysis -- techniques: miscellaneous -- catalogues -- surveys; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/staa1413
• Languages: English
• ISSNs: 0035-8711

Abstract

ABSTRACT: Time series data of celestial objects are commonly used to study valuable and unexpected objects such as extrasolar planets and supernova in time domain astronomy. Due to the rapid growth of data volume, traditional manual methods are becoming extremely hard and infeasible for continuously analysing accumulated observation data. To meet such demands, we designed and implemented a special tool named AstroCatR that can efficiently and flexibly reconstruct time series data from large-scale astronomical catalogues. AstroCatR can load original catalogue data from Flexible Image Transport System (FITS) files or data bases, match each item to determine which object it belongs to, and finally produce time series data sets. To support the high-performance parallel processing of large-scale data sets, AstroCatR uses the extract-transform-load (ETL) pre-processing module to create sky zone files and balance the workload. The matching module uses the overlapped indexing method and an in-memory reference table to improve accuracy and performance. The output of AstroCatR can be stored in CSV files or be transformed other into formats as needed. Simultaneously, the module-based software architecture ensures the flexibility and scalability of AstroCatR. We evaluated AstroCatR with actual observation data from The three Antarctic Survey Telescopes (AST3). The experiments demonstrate that AstroCatR can efficiently and flexibly reconstruct all time series data by setting relevant parameters and configuration files. Furthermore, the tool is approximately 3× faster than methods using relational data base management systems at matching massive catalogues.