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precor stsdas.analysis.dither


NAME · USAGE · PARAMETERS · DESCRIPTION · EXAMPLES · BUGS · REFERENCES
SEE_ALSO

NAME

precor -- remove cosmic rays.

USAGE

precor inlist

PARAMETERS

inlist [file name template]
Image(s) for cosmic ray cleaning.
(box_siz = 5) [int]
Box size -- 3 or 5 pixels.
(min_pix = 16) [int]
Required number of significant pixels.
(min_val = 3.0) [real]
Minimum value considered significant.
(ngroup = 4) [int]
Number of groups in image (for GEIS files only).
(do_sky = yes) [boolean]
Run sky task on the input image ?
(verbose = yes) [boolean]
Verbose output ?
(tempdir = "tmp$") [file]
Directory for temporary files.

DESCRIPTION

Precor is a task designed to remove the majority of cosmic rays from an image, while leaving astrophysical objects intact. The output image equals the input where astrophysical sources are detected, and is set to zero otherwise. Precor was developed as a preparatory step before cross-correlating images. For instance, in the case of deep raw WFPC2 images, the primary source of noise in the cross-correlation image may be due to cosmic rays. Precor dramatically reduces this source of noise.

Precor works by determining the number of pixels in a user defined box of size "box_siz" which have a value above "min_val". If this number equals or exceeds "min_pix", then the box and its neighboring pixels are retained in the output image. Otherwise the pixels in the box will be zero in the output image, unless they survive through inclusion in another, overlapping, box.

If the image has not been previously sky subtracted, it is recommended that the user set the "do_sky" option to "yes".

Given an input image called "input_image", precor creates an output image "input_name_obj".

Cross-correlation is only statistically optimal for determining a shift or rotation between an image when the background noise is Gaussian (see article cited below). Furthermore, there is evidence that converting to the signal-to-noise also reduces the effects on the cross-correlation of undersampling, particularily when determining a rotation. The true signal-to-noise of an image typically grows linearly with signal strength near the detection limit, and then as the square-root of the signal, as the Poisson noise of the object begins to dominate over the sky noise. In this task we provide the option of taking the square-root of the image (by setting "sig2n" to "yes). This only truely estimates the signal-to-noise under the assumption that images are dominated by Poisson noise. However, tests on WFPC2 images have found that there is essentially no difference between cross-correlating the true signal-to-noise images and the square-root images in terms of one's ability to determine a shift or rotation. This is not very surprising, as most of the power in a cross-correlation comes from the brightest objects, which typically are dominated by Poisson noise. We therefore have adopted this simplifying assumption.

EXAMPLES

BUGS

This version is a rather simple CL script that was initially developed for handling WFPC images only. Later on, several modifications were put in place to enable handling of NICMOS FITS files. This resulted in a less-than-perfect behavior in some cases. The task is simply unable to process OIF images (Old IRAF Format, ".imh"). Make sure your images conform either to GEIS format or FITS format with extensions and primary header (extensioin zero). If your input images are in GEIS (".??h") format, make sure the ngroup parameter trully describes the actual file contents.

REFERENCES

This task was written by A. Fruchter and I. Busko. A further description of its use, and a comparison of input and output images can be found in A. S. Fruchter, R. N. Hook, I. C. Busko, and M. Mutchler, 1997, "A Package for the Reduction of Dithered Undersampled Images", in "The 1997 HST Calibration Workshop", S. Casertano et al., ed. (Baltimore: Space Telescope Science Institute), in press. See http://www.stsci.edu/meetings/cal97.

SEE ALSO


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