Enhancing the Data Production of Astronomical Surveys

The increasing data collection and handling capabilities of the astronomer enable the creation of
large scale surveys such as the Sloan Digital Sky Survey (SDSS), Gaia or (in the future) the Large
Synoptic Survey Telescope (LSST). These projects move astronomical datasets in the petabyte
regime, orders of magnitude higher than what has previously been possible. The potential wealth of
information for the astronomer promises large scientific gains meaning that improvements in data
quality and processing efficiency have large potential returns. Future surveys such as LSST, will
likely be particularly important for the study of low mass X-ray binaries (LMXBS). They consist
of a black hole or neutron star accreting matter from a binary companion star. Although there are
thought to be thousands to millions of LMXBs in the Milky Way, only _200 systems have been
observed as the vast majority are obscured by Galactic gas and dust. The high optical sensitivity of
LSST, coupled with the synoptic nature of its observing strategy, makes it the ideal instrument for
discovering more of the LMXB population and characterising their parameters. In this work, the
potential for LSST to recover LMXB periods was investigated with a variety of potential observing
strategies. There was shown to be a factor of _3 improvement in period recovery when using alternative
strategies, rather than the current baseline. Gaia data on LMXBs was also investigated,
here prior information on the distribution on LMXBs was used to increase the accuracy of distance
estimates produced via parallax. Finally it was investigated whether provenance information could
be used to increase the processing efficiency of astronomical data processing. Whilst our results
demonstrated a net decrease in processing efficiency, significant increases were also found when
analysing the final data products.