We briefly describe some other useful tools.
Some Additional Tools
- Mosaic Coverage - MOSLST:
In doing a mosaic observation, you will want to good Fourier sampling (coverage)
as well as even sampling of the different pointing centres. Task moslst
produces a simple plot of LST vs point number for mosaiced visibility
files. In doing this, it required that you follow the field naming
convention mentioned earlier (see Section 21.3).
- Listing Mosaic Tables - IMLIST:
stores information concerning its linear mosaic
operation in the item
mostable (stored in both the
beam datasets). The table can be printed
by imlist, using
- Mosaic Point-Spread Function - MOSPSF:
It is occasionally instructive to look at the point-spread function at
a particular position in a mosaic experiment. Task mospsf
this. Apart from the input beam data-set, the user must specify a position and
frequency of interest (the point-spread function is also frequency
- Mosaic Sensitivity and Gain Images - MOSSEN:
Just as the point-spread function varies, so does the expected noise
level in an output mosaiced image. Additionally, as mentioned above,
does not attempt to completely correct for the primary
beam attenuation where there is too little data (i.e. some primary
beam attenuation is in the output image). The task mossen
can produce images of the expected rms noise and the remaining
primary beam attenuation given a mosaiced image.
- MOSTESS: mostess
implements a mosaicing algorithm
identical to the AIPS VTESS
program. It is more cumbersome
to use, and produces results which are no better than mosmem. Its
use is not recommended.
- PMOSMEM: pmosmem
can be used to do a joint deconvolution
of a polarimetric mosaic. The ``joint'' in this case applies to both
jointly processing the multiple Stokes parameters as well as
processing the different pointings. With polarised emission being potentially
positive and negative valued, the entropy measure for this is different
from a simple total intensity deconvolution. See
Sault et al. (1999)
(A&A 139, 387) for more background on joint polarimetric deconvolution.
- MOSCSDI: moscsdi, where the 'c' stands for 'Complex', does a
joint deconvolution of the combination of linear Stokes parameters Q+iU.
This approach avoids bias due to the position angle of the linear polarisation
and gives better results than mossdi for extended emission. See
Pratley and Johnston-Hollitt (2016)
for the details of the algorithm.