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## Search Algorithms

For projects which perform statistical analysis on groups of objects, it is important to rely on an objective search method to extract them from the data frames. For this reason plus the need to search large areas efficiently it is necessary to use automatic algorithms for this task. Several different methods are applied for this purpose depending on the demands for speed, limiting magnitude, and location of special objects. They fall in four main categories depending on their detection criterion, namely : level, gradient, peak, and template match detection.

The simplest and fastest method is using a given level over a previously determined background value as the criterion to identify possible sources. All pixels with intensities over this value are flagged and later grouped together to form objects (Pratt 1977). The background estimation can be avoided by using a gradient of the intensity distribution (Grosbøl 1979) instead. If the background variation over small areas can be regarded as linear, a Laplacian filter (see Equation 2.20) will locate only sharp features such as edges of stars. Since the derivative has a larger noise than the original image, this method will be slightly less sensitive than using the level. It can be applied directly to data without first having to compute the background and may therefore be faster to use if only point sources should be detected. The peak detection method finds pixels which are higher than their surroundings and is also based on a derivative (Newell and O'Neil 1977; Herzog and Illingworth 1977). Especially in crowded fields where a background is difficult to determine and where objects may overlap, it is a better search criterion than the two previous schemes. Finally, it is possible to compare each position with a template (e.g. the PSF) and thereby determine the probability of having an object there. Although this gives the most sensitive search criterion because it uses all information, it requires much larger amounts of computer time than the other methods.

Next: Fitting of data Up: Extraction of Information Previous: Extraction of Information
Petra Nass
1999-06-15