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J. Bland-Hawthorn
Anglo-Australian Observatory, P.O. Box 296,
Epping, N.S.W. 2121 Australia
S. Serjeant
Department. of Astrophysics, University of Oxford, Oxford
OX1 3RH, UK
P. L. Shopbell
Department. of Space Physics & Astronomy, Rice University,
P.O. Box 1892, Houston, TX 77251
We have obtained dark frames using the Tek 10241024 CCD at the
AAT 3.9m with varying exposure lengths (15, 30, 60, and 120min) and read-out
times (FAST, SLOW, XTRASLOW). The histogram
of each frame shows the contribution from the bias, read and dark noise. A
millisecond exposure was used to remove the bias and read noise contribution
to each histogram. The additional contribution from the dark noise is well
calibrated at 0.11countspix
ksec
. It is assumed that the
remaining events are related to cosmic rays. Only half the CCD frame was
used because long exposures revealed a weak intensity gradient
in the dark response on the other half.
Figure: Histogram of cosmic ray events in a two hour dark frame. The monotonic
curve is the cumulative histogram of these events. The error bars
are Poissonian and not independent.
Original PostScript figure (78 kB)
We define to be the number of cosmic ray events with energies
(expressed in counts) in the range
. The cumulative distribution
is then
When , the slope
of the plot
vs
is proportional to
.
In Figure
, the noisy histogram is the bias/dark/read noise
subtracted dark frame. The monotonic curve is a plot of
vs.
which is found to be rather well defined and reproducible over the different
exposures. We propose that de-glitch programs should compute the PDF in this
way for both the data and a matched dark frame (same exposure, same read-out
time). The PDF for the data is determined from all events identified by the
de-glitch algorithm. Since the energetic events are easier to find, the bright
end of both PDFs will be well matched. In Figure
, where the
de-glitch PDF turns over at low energy---presumably but not necessarily at an
energy greater than or equal to the turnover in the dark PDF---gives some idea
as to how effective the algorithm has been in removing the weaker events.
Figure: The cumulative histogram from Figure with which
to compare the performance of a de-glitch algorithm. Three cases are
illustrated: algorithm A is too conservative, algorithm B more reliable, and
algorithm C has mistaken real data with faint cosmic rays.
Original PostScript figure (16 kB)