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.help findgain Apr92 noao.nproto
.ih
NAME
findgain -- calculate the gain and readout noise of a CCD
.ih
USAGE
findgain flat1 flat2 bias1 bias2
.ih
PARAMETERS
.ls flat1, flat2
First and second dome flats.
.le
.ls bias1, bias2
First and second bias frames (zero length dark exposures).
.le
.ls section = "[*,*]"
The selected image section for the statistics. This should be chosen
to exclude bad columns or rows, cosmic rays and other blemishes, and
the overscan region. The flat field iillumination should be constant
over this section. Special care should be taken with spectral data!
.le
.ls center = "mean"
The statistical measure of central tendency that is used to estimate
the data level of each image. This can have the values: \fBmean\fR,
\fBmidpt\fR, or \fBmode\fR. These are calculated using the same
algorithm as the IMSTATISTICS task.
.le
.ls binwidth = 0.1
The bin width of the histogram (in sigma) that is used to estimate the
\fBmidpt\fR or \fBmode\fR of the data section in each image.
The default case of center=\fBmean\fR does not use this parameter.
.le
.ls verbose = yes
Label the gain and readnoise on output, rather than print them two per
line?
.le
.ih
DESCRIPTION
FINDGAIN uses Janesick's method for determining the gain and read noise
of a CCD from a pair of dome flats and a pair of bias frames (zero
length dark exposures). The task requires that the flats and biases be
unprocessed and uncoadded so that the noise characteristics of the data
are preserved. Note, however, that the frames may be bias subtracted
if the average of many bias frames is used, and that the overscan
region may be removed prior to using this task.
The section over which the statistics are computed should be chosen
carefully. The frames may be displayed and perhaps blinked, and
IMSTATISTICS, IMHISTOGRAM, IMPLOT, and other tasks may be used to
compare the statistics of sections of various flats and biases directly.
.ih
ALGORITHM
The formulae used by the task are:
.nf
flatdif = flat1 - flat2
biasdif = bias1 - bias2
gain = ((mean(flat1) + mean(flat2)) - (mean(bias1) + mean(bias2))) /
((sigma(flatdif))**2 - (sigma(biasdif))**2 )
readnoise = gain * sigma(biasdif) / sqrt(2)
.fi
Where the gain is given in electrons per ADU and the readnoise in
electrons. Pairs of each type of comparison frame are used to reduce
the effects of gain variations from pixel to pixel. The derivation
follows from the definition of the gain (N(e) = gain * N(ADU)) and from
simple error propagation. Also note that the measured variance
(sigma**2) is related to the exposure level and read-noise variance
(sigma(readout)**2) as follows:
.nf
variance(e) = N(e) + variance(readout)
.fi
Where N(e) is the number of electrons (above the bias level) in a
given duration exposure.
In our implementation, the \fBmean\fR used in the formula for the gain
may actually be any of the \fBmean\fR, \fBmidpt\fR (an estimate of the
median), or \fBmode\fR as determined by the \fBcenter\fR parameter.
For the \fBmidpt\fR or \fBmode\fR choices only, the value of the
\fBbinwidth\fR parameter determines the bin width (in sigma) of the
histogram that is used in the calculation. FINDGAIN uses the
IMSTATISTICS task to compute the statistics.
.ih
EXAMPLES
To calculate the gain and readnoise within a 100x100 section:
.nf
lo> findgain flat1 flat2 bias1 bias2 section="[271:370,361:460]"
.fi
To calculate the gain and readnoise using the mode to estimate the data
level for each image section:
.nf
lo> findgain.section="[271:370,361:460]"
lo> findgain flat1 flat2 bias1 bias2 center=mode
.fi
To calculate the gain and readnoise from several frames and accumulate
the results in a file for graphing:
.nf
lo> findgain.section = "[41:140,171:270]"
lo> findgain flat1 flat2 bias1 bias2 verbose- > gain.list
lo> findgain flat3 flat4 bias3 bias4 verbose- >> gain.list
lo> findgain flat5 flat6 bias5 bias6 verbose- >> gain.list
lo> findgain flat7 flat8 bias7 bias8 verbose- >> gain.list
lo> findgain flat9 flat10 bias9 bias10 verbose- >> gain.list
lo> plot
pl> graph gain.list point+
.fi
It is not obvious what to do with all the other combinations of flats
and biases. Note that the values in gain.list could have been averaged
or fit as well.
.ih
BUGS
The image headers are not checked to see if the frames have been
processed.
There is no provision for finding the "best" values and their errors
from several flats and biases.
.ih
SEE ALSO
findthresh, imstatistics, imhistogram, implot
.endhelp
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