MOPEX Online Manual
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Modules: Mosaic Outlier
Output Directory Keyword: OUTLIER_DIR
Default Output Directory: <output_dir>/Outlier
Depends on: Mosaic Interpolate
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_OUTLIER_FOR_RMASK trigger in the namelist and set the RMask_Fatal_BitPattern to use bit 1. Note that this is not the same as setting it to a value of 1. See Appendix 2: Fatal Bit Patterns for more information.
PURPOSE
-
This module uses the multiframe outlier rejection method. For a stack
of input images with good coverage, the mean and sigma at each pixel
on the sky are computed. Pixels outside an asymmetric sigma envelope are flagged
as outliers. If the data have good coverage (>10), this is the
best option for finding outliers. For very shallow coverage, this
method will not work well.
PARAMETER BLOCK
-
&MOSAICOUTLIERIN
BOTTOM_THRESHOLD = 3,
TOP_THRESHOLD = 3,
THRESH_OPTION = 1,
MIN_PIX_NUM = 3,
TILE_XSIZ = 500,
TILE_YSIZ = 500,
&END
INPUTS
THRESH_OPTION: (int) two options for this parameter: 1: "less aggressive" or 2: "more aggressive". The difference lies in the specific algorithm used to estimate sigma. We strongly recommend that all users use THRESH_OPTION = 1.
BOTTOM_THRESHOLD: (float) specifies the upper envelope in sigma to identify outliers from the pixel stack. Default value is 3.0
TOP_THRESHOLD: (float), specifies the lower envelope in sigma for selecting outliers.MIN_PIX_NUM: (int) Minimum number of pixels in the stack needed to estimate sigma.
TILE_X(Y)SIZ: (int) set these to a smaller size to avoid memery allocation problem if users have a very large mosaic. See the discussion of Tiles for details.
OUTPUTS
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Outlier Output FITS files
(interp_*_outlier.fits): The product of this step is an
outlier map. The pixel value is the deviation of that pixel in the
input image from the mean in the stack in terms of the number of
standard deviations
DISCUSSION
This module does multi-frame outlier detections and therefore requires images interpolated to a common grid. This method uses the input images interpolated for the subsequent co-addition into a mosaic and it represents temporal filtering. For a given pixel position in the interpolated grid, all pixel values from different interpolated images are processed to give a trimmed mean and standard deviation. The pixel values outside of the user-specified asymmetrical multi-sigma envelope are classified as outliers. This method detects both moving objects and radhits. It is not meant to be used in the cases of shallow coverage.
The thresholds - BOTTOM_THRESHOLD and TOP_THRESHOLD - can be set to 0, which is the default. In this case the decision of declaring a particular pixel an outlier is put off until running Mosaic RMask. The advantage of doing it this way is that the user can experiment with different thresholds for outliers without having to rerun mosaic_outlier module.
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