MOPEX Online Manual
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Modules: Mosaic Dual Outlier
Output Directory Keyword: DUAL_OUTLIER_DIR
Default Output Directory: <output_dir>/DualOutlier
Depends on: Detect (Outlier); Mosaic Projection
Important Notes: Using this module does not mean that MOPEX will automatically use the results for outlier detection. In order to use the results from this module, you must include the USE_DUAL_OUTLIER_FOR_RMASK trigger in the namelist and set the RMask_Fatal_BitPattern to use bit 2. Note that this is not the same as setting it to a value of 2. See Appendix 2: Fatal Bit Patterns for more information.
PURPOSE
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This module takes the outputs from Detect (Outlier) and Mosaic Projection and
applies the criteria to classify outlier using both spatial and
temporal information.
PARAMETER BLOCK
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&MOSAICDUALOUTLIERIN
MAX_OUTL_IMAGE = 2,
MAX_OUTL_FRAC = 0.95,
TILE_XSIZ = 500,
TILE_YSIZ = 500,
&END
INPUTS
MAX_OUTL_IMAGE: (int) The maximum number of images in which a potential outlier can be present and still be declared a dual outlier. Default value is 2. This value should be smaller than the total number of coverage at each pixel.
MAX_OUTL_FRAC: (float) This parameter specifies the maximum fraction of the images in which a potential outlier can be present and still be declared a dual outlier, i.e. the number of images containing outlier pixels divided by the total stack of input BCD images.
Tile_X(Y)SIZ: (int) The X (Y) size of an image tile used for this module. When the number of input images is very large, the recommended option is to use smaller Tile size than the total number of pixels of the final output mosaic image, in order to avoid memory shortage. The mosaic may be broken up into tiles in order to avoid memory allocation problems if the mosaic is very large.
OUTPUTS
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Dual Outlier Images
(proj_*_detmap_dual_outlier.fits): The output images which
have outlier pixels flagged. Outliers have negative values and real
sources have positive values.
DISCUSSION
Outlier rejection employs a complicated set of algorithms. The basic concept of dual outlier detection is to use both spatial and temporal filtering method. If you don't have good coverage or you want to make sure the edges of your mosaic image are clean of outliers, you should use this option.
The output outlier maps have likely outliers flagged on a pixel by pixel basis. However, clusters may contain both positive and negative identifications. The Level module next determines whether each cluster should be considered an outlier or not in its entirety.
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