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mscfindgain mscred


NAME · SYNOPSIS · USAGE · PARAMETERS · DESCRIPTION · ALGORITHM
EXAMPLES · BUGS · REVISIONS · SEE_ALSO

NAME

mscfindgain -- calculate the gain and readout noise of a mosaic of CCD

SYNOPSIS

MSCFINDGAIN uses Janesick's method for determining the gain and read noise in a CCD from a pair of dome flat exposures and a pair of zero frame exposures (zero length dark exposures).

USAGE

mscfindgain flat1 flat2 zero1 zero2

PARAMETERS

flat1, flat2
First and second mosaic dome flats.
zero1, zero2
First and second zero frames (zero length dark exposures).
extname = ""
List of extension names for which the gain is to be determined. If a blank list is specified then all extensions are analyzed.
mask = "BPM"
Bad pixel mask to use in excluding bad pixels. When there are multiple extensions the mask should be specified through the BPM header keyword.
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. The sections are applied to all the selected extensions. To use different sections the task must be run using the extname parameter to select specific extensions for the desired statistics section. Note that bad pixel masks is a better method of selecting 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.
nclip = 3
Number of sigma clipping iterations. If the value is zero then no clipping is performed.
lclip = 4, uclip = 4
Lower and upper sigma clipping factors used with the mean value and standard deviation to eliminate cosmic rays and unmasked bad pixels. Since mscfindgain is sensitive to the statistics of the data the clipping factors should be symmetric (the same both above and below the mean) and should not bias the standard deviation. Thus the values should not be made smaller than around 4 sigma otherwise the gain and readnoise estimates will be affected.
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?

DESCRIPTION

MSCFINDGAIN uses Janesick's method for determining the gain and read noise in a CCD from a pair of dome flat exposures and a pair of zero frame exposures (zero length dark exposures). This task operates on mosaic exposures in multiextension format. The extname parameter may be used to select all extensions, a single extension, or some subset of extensions.

The task requires that the flats and zeros 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 zero frames is used, and that the overscan region may be removed prior to using this task.

Bad pixels should be eliminated to avoid affecting the statistics. This can be done with bad pixels masks and sigma clipping. Alternatively an image section (which is the same for all extensions) may be chosen. The sigma clipping should not significantly affect the assumed gaussian distribution while eliminating outlyers due to cosmic rays and unmasked bad pixels. This means that clipping factors should be symmetric and should have values four or more sigma from the mean.

ALGORITHM

The formulae used by the task are:

    flatdif = flat1 - flat2

    zerodif = zero1 - zero2

       gain = ((mean(flat1) + mean(flat2)) - (mean(zero1) + mean(zero2))) /
	      ((sigma(flatdif))**2 - (sigma(zerodif))**2 )

   readnoise = gain * sigma(zerodif) / 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 zero 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. Mscfindgain uses the imstatistics task to compute the statistics.

EXAMPLES

To calculate the gain and readnoise within a 100x100 section:

    ms> mscfindgain flat1 flat2 zero1 zero2 section="[271:370,361:460]"

To calculate the gain and readnoise using the mode to estimate the data level for each image section:

    ms> mscfindgain.section="[271:370,361:460]"
    ms> mscfindgain flat1 flat2 zero1 zero2 center=mode

The effects of cosmic rays can be seen in the following example using artificial noise created with the artdata.mknoise package. The images have a gain of 5 and a readnoise of 10 with 100 cosmic rays added over the 512x512 images. The zero level images have means of zero and the flat field images have means of 1000. The first execution uses the default clipping and the second turns off the clipping.

    ms> mscfindgain flat1 flat2 zero1 zero2
    MSCFINDGAIN:
      mask = BPM, center = mean, binwidth = 0.1
      nclip = 3, lclip = 4., uclip = 4.

      Flats      = flat1[im1]  &  flat2[im1]
      Zeros      = zero1[im1]  &  zero2[im1]
      Gain       =  5.01 electrons per ADU
      Read noise = 10.00 electrons

      Flats      = flat1[im2]  &  flat2[im2]
      Zeros      = zero1[im2]  &  zero2[im2]
      Gain       =  5.00 electrons per ADU
      Read noise = 10.01 electrons
    ms> mscfindgain flat1 flat2 zero1 zero2 nclip=0
    MSCFINDGAIN:
      mask = BPM, center = mean, binwidth = 0.1
      nclip = 0, lclip = 4., uclip = 4.

      Flats      = flat1[im1]  &  flat2[im1]
      Zeros      = zero1[im1]  &  zero2[im1]
      Gain       =  2.86 electrons per ADU
      Read noise = 189.5 electrons

      Flats      = flat1[im2]  &  flat2[im2]
      Zeros      = zero1[im2]  &  zero2[im2]
      Gain       =  1.95 electrons per ADU
      Read noise = 127.8 electrons

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 zeros.

REVISIONS

MSCFINDGAIN - V4.1: December 5, 2000
New parameters to allow specifying bad pixel masks and sigma clipping were added. The output format was also improved.
MSCFINDGAIN - V4.0: August 22, 2000
This task is new in the version.

SEE ALSO

nproto.findgain, findthresh, imstatistics, imhistogram, implot


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