  Large astronomical databases obtained from sky surveys such as the
SuperCOSMOS Sky Surveys (SSS) invariably suffer from a small number of spurious
records coming from artefactual effects of the telescope, satellites and junk
objects in orbit around earth and physical defects on the photographic plate or
CCD. Though relatively small in number these spurious records present a
significant problem in many situations where they can become a large proportion
of the records potentially of interest to a given astronomer. In this paper we
focus on the four most common causes of unwanted records in the SSS: satellite
or aeroplane tracks, scratches fibres and other linear phenomena introduced to
the plate, circular halos around bright stars due to internal reflections
within the telescope and diffraction spikes near to bright stars. Accurate and
robust techniques are needed for locating and flagging such spurious objects.
We have developed renewal strings, a probabilistic technique combining the
Hough transform, renewal processes and hidden Markov models which have proven
highly effective in this context. The methods are applied to the SSS data to
develop a dataset of spurious object detections, along with confidence
measures, which can allow this unwanted data to be removed from consideration.
These methods are general and can be adapted to any future astronomical survey
data.
