Other
Scientific paper
Aug 2006
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006iaujd..10e..16i&link_type=abstract
Progress in Planetary Exploration Missions, 26th meeting of the IAU, Joint Discussion 10, 21-22 August 2006, Prague, Czech Repub
Other
Scientific paper
The number of glitches of cosmic rays (CRs) on images made by different cameras (HRI, MRI, and ITS) during the flight of Deep Impact to Comet Tempel 1 was studied. We analyzed the work of several codes (imgclean, crfind, di_crrej, rmcr) written by several authors and recognizing CRs on one image. For automatic removal of CRs on many images, we used imgclean, but for analyzing concrete images, other codes can be better. Our interactive code imitsr allows a user to choose the regions on a considered image where glitches detected by imgclean as CRs are ignored. In other regions chosen by the user, the brightness of some pixels is replaced by the local median brightness if the brightness of these pixels is greater by some factor than the median brightness. Our code rmcr is the only code among mentioned above which allows to work with raw images. In some cases (e.g., for removal of CRs near a bright star), it works better than other above codes, but for many calibrated images it has no advantages. For most HRI and MRI visual images made during low solar activity at exposure time t>4 s, the number Nsc of clusters of bright pixels on an image per second per cm^2 of CCD was about 2-4, both for dark and normal sky images. At high solar activity, Nsc sometimes exceeded 10. The ratio of the number of CRs consisting of n pixels obtained at high solar activity to that at low solar activity was greater for greater n. Due to higher variations of brightness of background, at default parameter settings, all the codes considered detected too much false CRs on ITS images. Clusters consisted of less that 4 pixels, usually can not be surely identified as CRs on ITS CCDs at any parameters, as the brightness for such small CRs is low enough. The number of clusters detected as CRs on a single infrared image is by a factor of several greater than the actual number of CRs; the number of clusters based on analysis of two successive frames is in agreement with an expected number of CRs. Some glitches of false CRs include bright pixels presented on different infrared images. For studies of CRs on infrared images, we used our code checkcr, which finds CRs based on comparison of two images and bad pixel maps.
A'Hearn Michael F.
Deep Impact Team
Desnoyer Mark
Ipatov Sergei I.
Klaasen Kenneth P.
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