Coming high-cadence wide-field optical telescopes will image hundreds of thousands of sources per minute. Besides inspecting the near real-time data streams for transient and variability events, the accumulated data archive is a wealthy laboratory for making complementary scientific discoveries.

The goal of this work is to optimise column-oriented database techniques to enable the construction of a full-source and light-curve database for large-scale surveys, that is accessible by the astronomical community.

We adopted LOFAR's Transients Pipeline as the baseline and modified it to enable the processing of optical images that have much higher source densities. The pipeline adds new source lists to the archive database, while cross-matching them with the known cataloguedsources in order to build a full light-curve archive. We investigated several techniques of indexing and partitioning the largest tables, allowing for faster positional source look-ups in the cross matching algorithms. We monitored all query run times in long-term pipeline runs where we processed a subset of IPHAS data that have image source density peaks over 170,000 per field of view (500,000 deg−2).

Our analysis demonstrates that horizontal table partitions of declination widths of one-degree control the query run times. Usage of an index strategy where the partitions are densely sorted according to source declination yields another improvement. Most queries run in sublinear time and a few (<20%) run in linear time, because of dependencies on input source-list and result-set size. We observed that for this logical database partitioning schema the limiting cadence the pipeline achieved with processing IPHAS data is 25 s.

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Astronomy and Computing
LAD: Layered Astronomical Databases
Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands

Scheers, B., Bloemen, S., Mühleisen, H., Schellart, P., Van Elteren, A., Kersten, M., & Groot, P. J. (2018). Fast in-database cross-matching of high-cadence, high-density source lists with an up-to-date sky model. Astronomy and Computing, 23, 27–39. doi:10.1016/j.ascom.2018.02.006