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Now you can image the data. HORUS and UVMAP both Fourier transform
the visibilities and create a dirty image and a dirty beam.
Alternatively, you could use MX which images and optionally
deconvolves with CLEAN. MX can image up to 16 fields in the primary
beam simultaneously (hence its name, because the MX missile makes [or
used to] multiple warheads) whereas UVMAP and HORUS can only do one
at a time. I recommend that you do not deconvolve (CLEAN or MEM)
blindly. Until you become familiar with the source that you are
imaging, always make a dirty image first and inspect it for
emission. You can tell MX to make a dirty image without CLEANing, and
then restart it for the CLEAN step later (see § 16). MX\
is very useful for discrete and widely separated emission, because you
can use the multi-field capability to make small images centred on the
sources of emission, rather than having to make one huge image. MX\
also has advantages in the CLEAN step (see § 16). Note that
there is no MX like program in MIRIAD.
The inputs for HORUS, UVMAP, and MX (for just the imaging stage) contain
many of the same adverbs, so I will just show the UVMAP adverbs, and any
additional ones for the other tasks that might cause confusion. Note of
course, that if you use HORUS on a multi-source file, you should turn on the
relevant calibration adverbs. You must apply the calibration just as in
SPLIT (§ 12), so I will not discuss the calibration adverbs
again here. For single-source files, the calibration adverbs should be turned
off (as there are no calibration tables to apply anymore) For MX, I will
defer discussion of the CLEAN step adverbs to the deconvolution section
(except to explain how to turn the CLEAN off). Finally, note that MX also
creates what is called a visibility work file. You will need this in the CLEAN step
later on, so don't delete it. Now, on with the nitty gritty.
- Important adverbs in the imaging tasks are
imsize and cellsize. The former is the size of the image in
pixels and should be a power of 2. The latter is the size of each pixel
in arcseconds. Both specify an x and a y dimension. They needn't
be the same (e.g. if your beam is elongated significantly in the
north-south direction). The product of imsize and cellsize
should be large enough to encompass the emission. If you are using
UVMAP or HORUS, this emission must fit into the inner quarter of the
image for correct deconvolution. If you are using MX then it can fill
virtually the entire image and still be succesfully deconvolved. Choose
the cellsize by allowing about 3 pixels across the FWHM of the
main lobe of the dirty synthesised beam. If you under-sample the beam,
you will induce some unpleasant effects in your images, especially if
you plan to deconvolve. If you do not plan to deconvolve, then you
could probably (but only if necessary) set the pixel size so that there
are 2 pixels across the FWHM of the synthesized beam without sacrificing
too much.
Consider also the effect of making an image from too few visibilities.
You are not doing anything very meaningful if you make an image with
more pixels than you have visibilities to constrain the pixel values.
This can easily be the case with ATCA snapshot data and large images.
Since , the size of the gridded
(u,v) plane is wavelengths. This must be big enough
to encompass your longest spacings. If you find the imaging programs tell
you that they have not used all of the visibilities (they may say
``Note: using only 1 of 345430 visibilities'' or the like), you may have set
the cell size to too large a value. Essentially you have not
matched your resolution to the resolution expected from the longest spacing.
This means you would throw away some of your data.
- As mentioned earlier, it is always a good idea to make a low
resolution image of the entire primary beam of new fields. This is done
by weighting (tapering) the short spacing data more than the longer
spacing data and imaging with a larger cell size. With the ATCA, unless
you are imaging a visibility file with multiple configurations, you do
not have a lot of spacings to play with, so you should do this rather
carefully. If you taper too heavily, you will weight down too much data
and there will be insufficient left to make a decent image. The
tapering procedure multiplies the visibilities by a Gaussian, and you
specify, via uvtaper, its 30% level in in the u and
v directions. See the HELP file for more information.
- As well as applying a Gaussian taper, you also have control over
how the visibilities are weighted through the uvwtfn adverb. You
can select uniform or natural weighting. When gridding, N
visibilities contribute to a `summed' (really convolved and resampled)
visibility in each grid cell. Uniform weighting means that the gridded
visibility is normalized by N; it is weighted down by the local
density of points. This means that each gridded cell contributes
`uniformly' to the Fourier transform. In natural weighting, the
normalization by N is not done, so that each gridded visibility
contributes its `natural' weight to the Fourier transform. Uniform
weighting gives slightly higher resolution and a better sidelobe
response. Natural weighting gives better sensitivity and is generally
preferred for detection experiments. The default is uniform weighting.
- uvbox controls the width of the box used in normalizing the
visibilities for uniform weighting. The default of zero means that the
normalization is determined from the number of ungridded points falling
in each grid cell. If uvbox=1 then the total number of ungridded
points in a box is found. If uvbox=2 the sum is done
in a box. These bigger boxes do what is called
super-uniform weighting. They are sometimes useful for sparsely sampled
data. Note that if the box was as big as the longest spacing, then you
would have natural weighting again, as all the visibilities would be
normalized by the same number. Experiment with caution here.
- A somewhat capricious adverb is zerosp. This allows you to
specify the total flux in the primary beam (should you know it from
single-dish observations) and its weight. The total power is known as
the `zero spacing', because this is what the visibility function sampled
at u=0 and v=0 corresponds to. It is the source of a great many
problems in interferometry because we can't measure it. It causes
sources that have spatial frequencies larger than that corresponding to
the shortest spacing to sit in negative bowls in the image. Any
deconvolution algorithm (CLEAN or MEM say) actually tries to
extrapolate the visibility function to the zero spacing value.
However, if you provide the correct value, the interpolation
through the unsampled part of the visibility function (at spacings less
than the shortest baseline) is helped considerably. If the visibility
function is very steep for the innermost spacings, the presence of a
correct zero spacing value can prove a great advantage. But there are
limits. If the correct zero spacing is more than about 4-5 times the
amplitude of the visibility on the shortest baseline, you will be asking
too much of the zero spacing insertion.
A more difficult problem is that you must also provide a weight
for the zero spacing, and the correct weight has proved rather elusive
in the past. One recommended recipe (Jacqueline van Gorkom) is that the
weight should be the number of gridded cells in the unsampled region of
the (u,v) plane interior to the shortest baseline in the data. Trial
and error is usually the way to do it. The effect on the dirty map is
simply to add an offset (as the Fourier transform of a delta function is
a constant). This will almost be indiscernible. The benefit comes in
the deconvolution stage, so the loop is rather tedious (see
§ 16) if you get it wrong a few times. For your first pass,
leave zerosp out, but remember it if need be. It's potentially
very useful.
- UVMAP, HORUS, and MX can all make a set of channel images for
spectral-line work, although the channel selection adverbs are all
rather inconsistently used and confusingly defined. However, since I am
only discussing imaging of one channel here, there is some hope of
getting it right. I have treated the channel adverbs, in particular, in
separate adverb boxes.
- If you want to move the centre of the image from the observing
phase centre, then set shift for the shift in x and y in
arcseconds on the sky. Positive values shift the image centre to the
North and East.
- The adverb uvrange allows you to exclude baselines outside
of a certain uv-distance (). For example, you may
have a short ATCA configuration such as the 750 m configuration as well
as the 6 km antenna. This leaves a very large gap between the spacings
involving the 6 km antenna and all the rest. To image just the short
spacings you could exclude antenna 6 with this adverb. For example,
at 6 cm, 750 m corresponds to 12.5 K, so setting
uvrange=0,20 would include the short spacings and exlcude
all baselines involving antenna 6.
- Make sure you turn on the grid correction with dogridcr.
- An amusing diversion is to view the gridded tracks on the TV. If
you would like to see this, set dotv=1.
- xtype and ytype specify the type of convolving function used
in the gridding process. The recommended function is spheroidal, for which
these adverbs should both be set at 5. This ensures maximum rejection
of aliasing.
UVMAP |
inname,inclass | Select the XY sorted |
inseq,indisk | visibility data |
channel=0 | For channel 0 data |
channel=18 | or select channel |
nmaps=1 | One image only |
outname,outdisk | Output image name |
outseq, outdisk | |
stokes='I' | Image Stokes I |
imsize=512,512 | Image size (pixels) in x and y (power of 2) |
cellsize=1,1 | Cell size in arcseconds in x and y |
shift=0 | Don't shift image centre from phase centre |
uvtaper=0 | No taper |
uvrange=0 | Include all baselines |
uvwtfn=' ' | Uniform or |
uvwtfn='na' | natural weighting |
uvbox=0 | Mimimum width for convolution function |
dogridcr=1 | Must make grid correction |
dotv= | Optionally put gridded data image on TV |
zerosp=0 | Include total flux density of all |
| sources in the field if you know it |
xtype=5 | Convolution function type for |
ytype=5 | gridding, use spheroidal function |
xparm=0 | Additional convolution |
yparm=0 | parameters |
baddisk=0 | AIPS disks to not put scratch files on |
The channel adverbs are slightly different for HORUS. In addition, you
have the potential to average IFs together. For example, you may have
set the two IFs to observe closely spaced frequencies in the same band
and you would like to grid and image them together. Just set
optype='sum' and select the desired IFs with bif and eif.
If you only select one IF, then optype is ignored.
HORUS |
| Turn on the calibration adverbs |
| for multi-source files |
bchan=0 | Image only one channel. 0 |
echan=bchan | for channel 0 data, or select |
chinc=1 | channel explicitly |
bif=1 | Select IFs to grid together |
eif=2 | and set the optype |
optype='sum' | to grid IFs together |
Because MX can make up to 16 images at once, some of the adverbs are
used slightly differently. Note that shift is replaced by
rashift and decshift, which allow you to specify a shift for each
field, the number of which is given by nfield. You might want to
use this option if it is not possible to fit all the emission present in
the primary beam into one reasonably sized image (1024 pixels or less),
and the emission is reasonably discrete. The channel adverbs are
different again too. Once again you can grid IFs together, but this time,
you just need to specify them with bif and eif, there is
no equivalent to the optype adverb of HORUS.
MX |
in2name,in2class | Name of visibility work file; leave blank |
in2seq,in2disk | when imaging only |
bif=2 | Select desired IF |
eif=2 | |
bchan=0 | Image only one channel. 0 |
echan=bchan | for channel 0 data, or select |
chinc=1 | channel explicitly |
npoints=1 | One image only |
channel=0 | No CLEAN restart |
nfield=1 | Just one field for beginners |
fldsize=0 | For CLEAN step only |
rashift=0 | Don't shift image centre |
decshift=0 | from phase centre |
niter=0 | Don't CLEAN yet |
Next: Imaging one channel and
Up: Imaging one channel and
Previous: Sorting
nkilleen@atnf.csiro.au