The SWIFT BAT Software Guide Version 6.3 30 ... - HEASARC - Nasa
The SWIFT BAT Software Guide Version 6.3 30 ... - HEASARC - Nasa
The SWIFT BAT Software Guide Version 6.3 30 ... - HEASARC - Nasa
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D.2. <strong>BAT</strong>CELLDETECT 105<br />
D.2 <strong>BAT</strong>CELLDETECT<br />
D.2.1 NAME<br />
batcelldetect - perform source detection using the sliding cell method<br />
D.2.2 USAGE<br />
batcelldetect infile outfile snrthresh<br />
D.2.3 DESCRIPTION<br />
batcelldetect performs source detection on a sky image. <strong>The</strong> sliding cell method is used to locate<br />
regions of the image which are significantly different from the background.<br />
This tool is more appropriate for coded aperture imaging because: (1) it assumes Gaussian<br />
fluctuations, not Poissonian; (2) it measures the local background; and (3) it measures the local<br />
background standard deviation. A source is detected at a pixel if that pixel’s value exceeds the<br />
background by more than “snrthresh” times the background standard deviation. Users can increase<br />
the significance of a detection and reduce false detections by requiring more than one adjacent pixel<br />
exceed the threshold, using the “nadjpix” parameter. <strong>The</strong> “nullborder” and “bkgpcodethresh”<br />
parameters can also be used to exclude false positives near the edge of the image.<br />
<strong>The</strong> background value is estimated by using a sliding window. <strong>The</strong> shape of the window is<br />
either circular or square, and the radius (or half-width) is specified by the bkgradius parameter.<br />
In determining the background, a circular region at the center of the window is excluded, whose<br />
radius is the srcradius parameter. Thus, the background does not include contamination from the<br />
source region.<br />
In a single iteration, the sliding cell algorithm is less sensitive in a region around bright sources,<br />
because the background standard deviation becomes biased. To avoid this, the algorithm can be<br />
run in multiple iterations. After each iteration, the detected pixels are removed, and thus the bias<br />
can be significantly reduced.<br />
A second stage fits a point spread function to regions of the image where sources are detected.<br />
For new sources, this aids in refining the centroid of the source position, as well as in estimating<br />
uncertainties.<br />
<strong>The</strong> default output is a catalog list of detected sources, plus various statistics about them. <strong>The</strong><br />
output flux column is either COUNTS or RATE, depending on the input image units. <strong>The</strong> tool can<br />
optionally output the background map, the background fluctuation map, and a significance map.<br />
IMPORTANT NOTE: if the keepbadsources parameter is ‘YES’, then the output catalog<br />
may contain some sources which failed processing for one reason or another. Information on the<br />
success or failure on a source-by-source basis is recorded in the DETECT STATUS column.<br />
<strong>The</strong> user can also supply an input catalog. Sources in the input catalog, which are within the<br />
field of view of the image, are assumed to be fixed at their known positions, and fitted during