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ldss3-pattern

ldss3-pattern removes pattern noise from LDSS3 frames.


USAGEldss3-pattern framename
INPUT
framename
OUTPUT
framenameffcn.fits are the pattern noise subtracted files
PARAMETERS
dewar the relevant CCD configuration: LDSS3-2 or LDSS3-4
top amount of the data region (in pixels) at the top of each chip used to remove the pattern noise
intermediate output intermediate FITS files
iterations number of passes
scale1 smoothing pass 1
cut1 clipping pass 1
In order to utilize more or less passes you can change the number of iterations to the number of passes you would like to run. You must add or remove both a scale and cut for each pass. The parameter file included illustrates how you should add or remove passes.


Details:

The program ldss3-pattern estimates the contribution of the pattern noise at every location using a template, and then subtracts this contribution from an LDSS3 data frame. The Fourier Transform of an empty subsection of the data is taken and its power spectrum is computed. This is used as a template power spectrum for computing the contribution of the pattern noise over the entire data section of the array. Then the Fourier Transform of the full data section of the array is taken and its power spectrum is computed. Because the template power spectrum is computed from a smaller array, the template power spectrum is resampled to produce an array with a length equal to the power spectrum computed for the full data section of the frame. This rescaling of the size of the template power spectrum uses a simple interpolation routine because the rescaling in size is by a non-integer amount. Then a simple sigma-clipping routine is performed to identify regions of the data power spectrum that have significantly greater power than in the template. This excess power is due to the presence of real information in the data array on top of the pattern noise. By identifying these regions of the power spectrum that arise from real signal, we go back to the original sine and cosine components of the Fourier Transform of the data section and filter out those components identified by the power spectrum analysis. This should leave you with the pattern noise in Fourier space. A inverse Fourier Transform is performed to produce the pattern noise back in real space.

The above task is performed for the output of both amplifiers. Because the pattern noise is identical in both FITS files, we use a weighted average of the two results as the final estimate of the pattern noise. A weighted average is computed for each pixel for all chips of the pattern noise, each weight being inverse of the pixel value squared. This down-weights larger values, such as pixels containing real signal or other artifacts such as cosmic-rays, and these are effectively excluded from the resulting estimate of the pattern noise. From this a template of the pattern noise is created and then can be subtracted from the data.


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