Large astronomical data bases obtained from sky surveys such as the SuperCOSMOS Sky Survey (SSS) invariably suffer from spurious records coming from the artefactual effects of the telescope, satellites and junk objects in orbit around the 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. Accurate and robust techniques are needed for locating and flagging such spurious objects, and we are undertaking a programme investigating the use of machine learning techniques in this context. 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 haloes around bright stars due to internal reflections within the telescope and diffraction spikes near to bright stars. Appropriate techniques are developed for the detection of each of these. The methods are applied to the SSS data to develop a data set of spurious object detections, along with confidence measures, which can allow these unwanted data to be removed from consideration. These methods are general and can be adapted to other astronomical survey data.
This method was developed to remove four types of objects -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 the bright stars in the sky survey data using catalogue of objects as described in REF .
Cleaning sky survey data bases using Hough transform and renewal string approaches
Monthly Notices of the Royal Astronomical Society
Monthly Notices of the Royal Astronomical Society
Physics