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PISA - Position Intensity and Shape Analysis

PISA is a group of programs for automatically detecting and measuring objects on an astronomical image.

The main PISA program, PISAFIND, locates all the objects on an image by detecting any regions of pixels that are joined together, form a region greater than a given size and that have intensities that are significantly greater than the image background. Using these criteria reduces the likelihood of making false object detections. A set of parameters that quantify the position, intensity and shape of each of these regions -- the "objects" -- are then calculated. Objects that overlap with others (blends) can be separated out from each other and estimates of the parameters of the originals made.

The automated detection, deblending and parameterisation of objects on images (from sources such as small and large format CCDs and scanned photographic plates) is useful when the numbers of objects become quite large and the task of locating and measuring each by hand isn't practical. Some areas of astronomy where this is of particular use are the number counts of faint galaxies and stars and the study of galaxy distributions, however, automated measurement can also be of use in any studies that need to relate the colour and magnitude of many objects.

Figure 1: The results of a run of PISAFIND on an image (the galaxy cluster 0637-53). Note how the plotted objects are displayed overlaid on the image and how merged objects have been separated.

PISA also helps to classify objects into different astronomical types. This uses a "peakedness" estimate (that indicates how like a star an object is) together with other parameters that are intensity independent -- at least in principle. This explains why the objects detected by PISAFIND are coloured differently above, cuts in peakedness and ellipticity have been used to separate out stellar (green) from non-stellar (yellow) objects.

Figure 2: In addition to the detection mode shown in Figure 1, PISAFIND can also use an analytic stellar profile to fit stars. However, before this can be done certain characteristics of the stars on the image must be determined. This figure shows a fit of the analytic profile to a selection of good stars on the frame. Using a model fit is particularly useful when measuring low-intensity stars as the some of the information necessary for a good fit is already known (their shape).

Figure 3: The results from PISAFIND can also be used to generate model data to which noise can be added. This is useful for testing purposes. The figure above shows a false image generated from the data taken in Figure 1.

Figure 4: The objects detected by PISAFIND can be used by other Starlink packages. In this example the regions that the detected objects occupy have been removed using the KAPPA program ARDMASK. This makes the completeness of the detections obvious (840 where detected in this image, using a 2.5 sigma threshold), and also makes clear the flatness of the image background. This method could also be used as a precursor to creating flatfields from "dithered" images. The measurements made by PISA programs can also be imported into the catalogue manipulation package CURSA.

PISAFIND is based on the APM IMAGES program created by Mike Irwin of the University of Cambridge. The PISA package is fully described in Starlink User Note 109.

Questions or comments to: p.w.draper@durham.ac.uk.

Copyright © 2005 Central Laboratory of the Research Councils
Copyright © 2006 Particle Physics and Astronomy Research Council
Copyright © 2008-2009 Science and Technlogy Facilities Council
Copyright © 2009-2013 Peter W. Draper
Last modified: 02-Jun-2016
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