findgain flat1 flat2 bias1 bias2
flat1, flat2
First and second dome flats.
bias1, bias2
First and second bias frames (zero length dark exposures).
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 illumination should be constant
over this section. Special care should be taken with spectral data!
center = mean
The statistical measure of central tendency that is used to estimate
the data level of each image. This can have the values: mean,
midpt, or mode. These are calculated using the same
algorithm as the IMSTATISTICS task.
binwidth = 0.1
The bin width of the histogram (in sigma) that is used to estimate the
midpt or mode of the data section in each image.
The default case of center=mean does not use this parameter.
verbose = yes
Label the gain and readnoise on output, rather than print them two per
line?
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.
The formulae used by the task are:
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)
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:
variance(e) = N(e) + variance(readout)
Where N(e) is the number of electrons (above the bias level) in a given duration exposure.
In our implementation, the mean used in the formula for the gain may actually be any of the mean, midpt (an estimate of the median), or mode as determined by the center parameter. For the midpt or mode choices only, the value of the binwidth 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.
To calculate the gain and readnoise within a 100x100 section:
lo> findgain flat1 flat2 bias1 bias2 section="[271:370,361:460]"
To calculate the gain and readnoise using the mode to estimate the data level for each image section:
lo> findgain.section="[271:370,361:460]"
lo> findgain flat1 flat2 bias1 bias2 center=mode
To calculate the gain and readnoise from several frames and accumulate the results in a file for graphing:
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+
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.
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.
findthresh, imstatistics, imhistogram, implot,