COSMIC RAY REMOVAL UTILITY PACKAGE
Version : 1.4
Author(s) : Frank Valdes (firstname.lastname@example.org)
License : AURA
Website : http://iraf.noao.edu
Installs from Open Source Astronomy for Linux cd 2
Disk space required for installation is 1.68 Mb
The cosmic ray package provides tools for identifying and removing cosmic rays in images. The tasks are:
cosmicrays - Remove cosmic rays using flux ratio algorithm craverage - Detect CRs against average and avoid objects crcombine - Combine multiple exposures to eliminate cosmic rays credit - Interactively edit cosmic rays using an image display crfix - Fix cosmic rays in images using cosmic ray masks crgrow - Grow cosmic rays in cosmic ray masks crmedian - Detect and replace cosmic rays with median filter crnebula - Detect and replace cosmic rays in nebular data
The best way to remove cosmic rays is using multiple exposures of the same field. When this is done the task crcombine is used to combine the exposures into a final single image with cosmic rays removed. The images are scaled (if necessary) to a common data level either by multiplicative scaling, an additive background offset, or some combination of both. Cosmic rays are then found as pixels which differ by some statistical amount away for the average or median of the data.
A median is the simplest way to remove cosmic rays. This is an option with crcombine. But this does not make optimal use of the data. An average of the pixels remaining after some rejection operation is better. If the noise characteristics of the data can be described by a gain and read noise then cosmic rays can be optimally rejected using the "crreject" algorithm. This works on two or more images. There are a number of other rejection algorithms which can be used as described in the task help.
The rest of the tasks in the package are used when only a single exposure is available. These include interactive editing with credit. The replacement algorithms in this task may also be used non-interactively if you have a list of pixel coordinates as input. Other tasks automatically identifying pixels which are significantly higher than surrounding pixels.