Continuum polarimetry case

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Continuum polarimetry case

For the continuum data set, make a single-field, full-polarization (I, Q, U, & V), continuum image of the target source, 0957+561 (Field 10), using the Clark CLEAN deconvolution algorithm. To do this, first, set which source you want to image, use the imager.advise function to get an estimate of what the image properties should be, then set the image parameters and weight:

imgrC:=imager(filename='ap366.ms');  # Create imager tool if not already done. 
imgrC.setdata(mode='none',           # Select continuum data for the target 
              fieldid=10);           #  source in field 10 (0957+561)
imgrC.advise(takeadvice=F,           # Determine image and cell size using
             fieldofview='1arcmin'); #  advise function.  Do not take advice,
                                     #  note parameters in logger
                                     #  window and use as a basis for setimage.

The Advice function will produce logger messages which you can use to guide your choice of image properties:

  Advising image properties
  Maximum uv distance = 585844 wavelengths
  Recommended cell size < 0.176041 arcsec
  Recommended number of pixels = 360
  Dispersion in uv, w distance = 243745, 81981.6 wavelengths
  Best fitting plane is w = 0.345636 * u + 0.345275 * v
  Dispersion in fitted w = 24694.9 wavelengths
  Wide field cleaning is not necessary

Set the image parameters and weights:

imgrC.setimage(nx=512,               # Set image plane parameters
               ny=512,
               cellx='0.1arcsec',
               celly='0.1arcsec',
               stokes='IQUV',        #   (full polarization)
               fieldid=10);  
imgrC.weight(type='uniform');        # Set uniform weighting

At this point, you can examine the synthesized beam or Point Spread Function to determine the resolution resulting from the chosen weighting scheme and see what artifacts to expect from beam sidelobes during CLEANing (Fig. 1.17) (Note: this is not a required step to produce an image):

imgrC.makeimage(type='psf',          # Form the PSF image if desired
              image='0957+561.psf'); #  use viewer to see sidelobe response

imgrC.fitpsf(psf='0957+561.psf');    # Measure the beam size

The imgrC.fitpsf function will report the FWHM of the synthesized beam in the logger:

    Beam fit: 0.423026 by 0.335217 (arcsec) at pa 30.068 (deg)

$\begin{figure} % latex2html id marker 1146 \epsfig{file=cookbook.dir/vla.psfC.ps... ...plotted at -5, 5, 10, 30, 50, 70, \& 90\% of the peak.} \hrulefill \end{figure}$

CLEAN the image by first creating a dirty image first, define a mask around obvious source structure, clean for a number of iterations, and redefine the mask until you have all obvious source emission within the masked region. Then continue to clean until you have reached the noise level:

imgrC.clean(algorithm='clark',       # Image, and deconvolve using 
            niter=5000,              #  the Clark CLEAN
            gain=0.1,                #  Write the cleaned image to the file
            model='0957+561.mod',    #  0957+561.im on disk.
            image='0957+561.im',
            residual='0957+561.resid',
            mask='0957+561.mask',    # Clean mask file name. 
            interactive=T,           # Clean interactively, with the option
            npercycle=500);          #  to choose a new clean region every
                                     #  500 cleaning cycles.  Stop cleaning when 
                                     #  you have reached desired level. 
dv.gui();                            # Bring up default viewer tool to 
                                     #  examine final image.

During the CLEAN run, you will be asked to interactively define CLEAN regions (polygons or boxes) in a pop-up viewer. Details about how to do this is described in Section 1.12.3. Interactive masking allows you to add and subtract multiple polygon structures to create a mask around complex source structure. Fig. 1.18 shows an example of the interactive mask GUI interface that pops up during the CLEANing cycle.

Use the viewer to display the image 0957+561.im and obtain statistics (Fig. 1.19). The final image should have an RMS $\sim$ 0.45 mJy beam^-1 and an image peak $\sim$ 26.3 mJy beam^-1.

$\begin{figure} % latex2html id marker 1157 \epsfig{file=cookbook.dir/vla.maskC.p... ...ned at the start of the CLEANING process for 0957+561.} \hrulefill \end{figure}$

$\begin{figure} % latex2html id marker 1161 \epsfig{file=cookbook.dir/vla.0957.im... ...beam is shown in the bottom left corner of the image. } \hrulefill \end{figure}$

Note that the CLEAN component model is written as an image file. If necessary or desired, CLEANing may be restarted by specifying an existing (non-zero) model image, which will be subtracted before the deconvolution is resumed. This provides a simple means of monitoring progress of the deconvolution outside of the interactive mode. Note that the imager.setdata and imager.setimage functions need not be run again when restarting a CLEAN with the previously setup imager tool.

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