Imaging is a beast and we're using two different packages to do it. MSClean has failed us so far. Apparently our signal to noise isn't so good. Also, we want a beam that's double the size of our image.

Here are the steps:
  1. run IMAGR in AIPS.
    • cellsize 1.5
    • imsize 2720
    • robust 5 (or 1.5)
    • getn DBCON
  2. run SUBIM to cut out the piece of the cube and beam you want. AIPS will automatically round 2720 up to the nearest power of 2 (4096). Miriad can't handle files this big. You will have two steps: cut out a piece of the beam and a piece of the cube.
    • DEFAULT SUBIM
    • blc 1369 1369 0
    • trc 2728 2728 0
    • getn *ICL001; go subim
    • DEFAULT SUBIM
    • blc 689 689 <nchannels / 2>
    • trc 3408 3408 <nchannels / 2>
    • getn *IBM001; go subim
  3. Write these two files out to disk with FITTP (*.map.fits, *.beam.fits)
  4. Read the files into miriad using fits
    • task fits
    • in=*.map.fits
    • op=xyin
    • out=*.map
    • go fits;
    • task fits
    • in=*.beam.fits
    • op=xyin
    • out=*.beam
  5. Measure the noise in a line free channel (kvis, make a box, press s)
  6. Clean your map lightly with clean
    • task clean
    • map = *.map
    • beam = *.beam
    • out = *.clean
    • niters = ??
    • cutoff = 3 sigma?
  7. Restore the clean components to the map. You will have to run this a total of three times; once for the clean components map (C), once for the residual map (R), and once for the C+R map.
    • task restor
    • map = *.map
    • beam=*.beam
    • model = *.clean
    • mode=convol
    • out = *.cc.restor
    • go restor
    • mode = resid
    • out = *.resid.restor
    • go restor
    • unset mode
    • out= *.restor
    • go restor
  8. Smooth the *.cc.restor map with a gaussian twice the size of the beam
    • task convol
    • map=*.cc.restor
    • out=*.cc.restor.convol
    • fwhm=2*fwhm, 2*fwhm (from header.. just make this a circular beam, use the largest fwhm)
    • options=final
  9. Hanning smooth in velocity space. We should try to find a faster method because this takes a long time.
    • task hanning
    • in=*.cc.restor.convol
    • out=*.cc.restor.convol.hanning
    • width=3
  10. Blank the smooth cc cube
    • task maths
    • exp=<*.cc.restor.convol.hanninng>
    • mask = <*.cc.restor.convol.hanning>.gt.0.00001
    • out = *.mask
  11. Use cgcurs to define clean regions. It will output a file named cgcurs.region
    • task cgcurs
    • in=*.mask
    • type=pix
    • device=/xwin
    • range=0,0.001
    • nxy=1
    • options=region
  12. Rename the cgcurs.region file
    • mv cgcurs.region *.region
  13. mask the mask cube using immask to get rid of any spurious clean components
    • task immask
    • in=*.mask
    • region=@*.region
    • logic=and
    • flag=true
  14. Use this mask to re-clean your map. The inputs will be the same as before, except you will now have a mask and we will clean to 1 sigma (probably).
    • task clean
    • map = *.map
    • clean = *.clean
    • niters = 50000
    • cutoff = 1*sigma
    • out = *.mask.clean
  15. Restore your masked clean components, as before. We will run this three times (C, R, C+R maps).
    • task restor
    • map = *.map
    • beam = *.beam
    • model = *.mask.clean
    • options = convol
    • out = *.mask.cc.restor
    • go restor
    • options=resid
    • out = *.mask. resid.restor
    • go restor
    • unset options
    • out = *.mask.restor
    • go restor
  16. copy the dirty map to new file
    • cp -r *.map *.mask.map
  17. apply the mask to the dirty map and the residual map
    • task immask
    • in=*.mask.map
    • region=mask(*.mask)
    • flag=true
    • logic = and
    • go immask
    • in = *.mask.resid.restor
    • go immask
Now we have a cleaned mask! To find fluxes, we need to use the Jorsater-van Moorsel method.See DeterminingFluxes.

-- AdrienneStilp - 2009-03-03
Topic revision: r4 - 2009-03-18, StevenWarren
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