Renewal Strings for Cleaning Astronomical Databases

Abstract

Large astronomical databases obtained from sky surveys such as the SuperCOSMOS Sky Surveys (SSS) invariably suffer from 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. We have developed renewal strings, a probabilistic technique combining the Hough transform, renewal processes and hidden Markov models which has 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 other astronomical survey data.