Flatfielding

I compared means and midpoints for images; most were very similar, but some were different, so the images should be scaled.

I used flatcombine first for flats without a filter, then for those with the cuso4 filter.

noao.imred.ccdred.flatcombine 
                                 I R A F  
                             Image Reduction and Analysis Facility
PACKAGE = ccdred
TASK = flatcombine

input   =          @filterflats  List of flat field images to combine
(output =                 Flat) Output flat field root name

(combine=              average) Type of combine operation
(reject =              ccdclip) Type of rejection
(ccdtype=                 flat) CCD image type to combine
(process=                  yes) Process images before combining?
(subsets=                  yes) Combine images by subset parameter?
(delete =                   no) Delete input images after combining?
(clobber=                   no) Clobber existing output image?
(scale  =                 mode) Image scaling
(statsec=                     ) Image section for computing statistics
(nlow   =                    1) minmax: Number of low pixels to reject
(nhigh  =                    1) minmax: Number of high pixels to reject
(nkeep  =                    1) Minimum to keep (pos) or maximum to reject (neg)
(mclip  =                  yes) Use median in sigma clipping algorithms?
(lsigma =                   3.) Lower sigma clipping factor
(hsigma =                   3.) Upper sigma clipping factor
(rdnoise=              RDNOISE) ccdclip: CCD readout noise (electrons)
(gain   =                 GAIN) ccdclip: CCD gain (electrons/DN)
(snoise =                   0.) ccdclip: Sensitivity noise (fraction)
(pclip  =                 -0.5) pclip: Percentile clipping parameter
(blank  =                   1.) Value if there are no pixels
(mode   =                   ql)

I divided flats using imarith, imarith Flat / Flat2 Div (without filter / with filter)

Div does not have the dark spots found in all the flats. These are found at:

x y
962.75 54.25
1447.25 203.75
1662.25 59.25
1655.25 76.25
1643.25 113.25
1827.75 122.25
1973.75 117.25
2102.75 112.25
2200.25 145.25
2200.25 145.25
2338.75 197.25
2565.75 68.25
2493.25 169.25
2606.25 139.25

The flatfields without a filter (Flat) has a weird bump (see below), so the flatfield with the filter (Flat2) will probably be used.

I use response to normalize each flatfield

noao.twodspec.longslit.response 
                                    I R A F  

                    Image Reduction and Analysis Facility  
PACKAGE = longslit     
TASK = response    
calibrat=                Flat2  Longslit calibration images  
normaliz=                Flat2  Normalization spectrum images  
response=               nFlat2  Response function images  
(interac=                  yes) Fit normalization spectrum interactively?  
(thresho=                INDEF) Response threshold  
(sample =                    *) Sample of points to use in fit  
(naverag=                    1) Number of points in sample averaging  
(functio=              spline3) Fitting function  
(order  =                   13) Order of fitting function  
(low_rej=                   3.) Low rejection in sigma of fit  
(high_re=                   3.) High rejection in sigma of fit  
(niterat=                    1) Number of rejection iterations  
(grow   =                   0.) Rejection growing radius  
(graphic=             stdgraph) Graphics output device  
(cursor =                     ) Graphics cursor input  
(mode   =                   ql)

The spots are back! I think this is a good thing? (yes, it is)

noao.twodspec.longslit.response 
                                   I R A F    
                    Image Reduction and Analysis Facility  
PACKAGE = longslit     
TASK = response    
calibrat=                 Flat  Longslit calibration images  
normaliz=                 Flat  Normalization spectrum images  
response=                nFlat  Response function images  
(interac=                  yes) Fit normalization spectrum interactively?  
(thresho=                INDEF) Response threshold  
(sample =                    *) Sample of points to use in fit  
(naverag=                    1) Number of points in sample averaging  
(functio=              spline3) Fitting function  
(order  =                   13) Order of fitting function  
(low_rej=                   3.) Low rejection in sigma of fit  
(high_re=                   3.) High rejection in sigma of fit  
(niterat=                    1) Number of rejection iterations  
(grow   =                   0.) Rejection growing radius  
(graphic=             stdgraph) Graphics output device  
(cursor =                     ) Graphics cursor input  
(mode   =                   ql)

There are spots on this one also.

I used imarith to divide images, nFlat / nFlat2 nDiv

I then divided an individual flat by the combined flat, which made the spots disappear, so this means that they are not bad pixels; they just have a lower response.

I will use the normalized flat with filter, now renamed Flat.fits

I ran ccdproc again, changing the following parameters from when run for zero correction.
(flatcor= yes) Apply flat field correction?
(flat = Flat) Flat field images
(interac= no) Fit overscan interactively?
(order = 4) Number of polynomial terms or spline pieces
(grow = 0.) Rejection growing radius