View source for ACA Simulation (CASA 5.4)

[[Category: Simulations]]

To create a script of the Python code on this page see [[Extracting scripts from these tutorials]].
__NOTOC__
== ALMA 12m + ACA + Total Power ==

We can simulate ALMA observations that use the main array of 12 m antennas augmented by the ACA and Total Power antennas by generating simulated observations for each component separately, and then combining the Measurement Sets in {{simanalyze}}.  This technique is general and can be used to simulate observations using multiple 12 m array configurations, as well.  Total power observations can be simulated either in an independent run of simobserve, or along with an interferometric simulation.  Note that if you simulate total power and an interferometric observation simultaneously with simobserve, they must have the same set of pointing centers and the same integration and total time, which is probably not realistic. (For example it is generally recommended to observe a larger area by 1/2 primary beam in total power mode to combine with a 12 m ALMA mosaic).

=====Set simobserve as current task=====
Reset all parameters to default, and then set the project name to ''m51c''
<source lang="python">
# Set simobserve to default parameters
default("simobserve")
# Our project name will be m51c, and all simulation products will be placed in a subdirectory m51c/
project="m51c"
</source>

=====Specify sky model image=====
We'll use an Halpha image of M51 as a model of the sky, for this example.   The ''curl'' command will copy the file from a URL and rename it.

<source lang="python">
# Model sky = Halpha image of M51 
os.system('curl https://casaguides.nrao.edu/images/3/3f/M51ha.fits.txt -f -o M51ha.fits')
skymodel         =  "M51ha.fits"
</source>  

Note that {{simobserve}} will not modify your original input image.  Rather, it will make a copy ''m51c/m51c.skymodel''.

We will override most of the parameters in the Halpha FITS image to make the image more suitable to a sub-millimeter ALMA observation.  We will:
* place the source in the southern hemisphere with the ''indirection'' parameter, 
* set the pixel size to 0.1arcsec, to simulate an observation of a galaxy that is smaller in angular size. (M51 itself would require a quite large mosaic, and in any case we'd like the input model pixels to be significantly smaller than the synthesized beam.)
* set the peak brightness to 0.004 Jy/pixel
* set the frequency to 330GHz, and since it's a 2D image we'll set the single "channel" width to be 50MHz, and peak brightness of 0.004 Jy/pixel.  These parameters are plausible for observing a sub-mm emission line in a galaxy.
<source lang="python">
# Set model image parameters:
indirection="B1950 23h59m59.96s -34d59m59.50s"
incell="0.1arcsec"
inbright="0.004"
incenter="330.076GHz"
inwidth="50MHz"
</source>

----

====Simulate 12m interferometric observation====
[[Image:M51c.ALMA_0.5arcsec.skymodel.png|thumb|hexagonal mosaic overplotted on sky model]]

We'll begin by simulating the observation as seen by the main 12 m ALMA array.  We'll have {{simobserve}} calculate a hexagonal mosaic of pointings.

The interface for {{simobserve}} provides an <tt>integration</tt> parameter, which is the dwell time at each pointing in the mosaic.  We'll set that to 10 s.  A real observation might dwell on each pointing for ~20 s and record 3 sets of visibilities (i.e. 6 s integration time) for each pointing.  {{simobserve}} does not currently offer a capability to reproduce this behaviour exactly, but the difference in UV coverage should be very minor between the real observation and the simulated one.  You could try setting <tt>integration</tt> to ~20 s, which would more closely match the dwell time for some real observations.

<source lang="python">
# have simobserve calculate mosaic pointing locations:
setpointings       =  True
integration        =  "10s"
mapsize            =  "1arcmin"
maptype            =  "hex"
pointingspacing    =  "9arcsec"      # this could also be specified in units of the primary beam e.g. "0.5PB"
</source> 

=====Calculate Visibilities=====
When you provide a desired resolution in the <tt>antennalist</tt> parameter, {{simobserve}} will determine what array configuration to use.  
<source lang="python">
obsmode            =  "int"
antennalist        =  "ALMA;0.5arcsec"
totaltime          =  "3600s"
</source>

Run {{simobserve}}, displaying graphics to the screen and also writing graphics output to files that can be found in the project directory, ''m51c''.

<source lang="python">
graphics           =  "both"
simobserve()
</source>

----

====Simulate 12m total power observation====
[[Image:M51c.aca.tp.skymodel.png|thumb|rectangular map overplotted on sky model]]

Next we'll simulate a total power raster map of the same area, on a square grid.  CASA simulation tools can not simulate 
true on-the-fly mapping (with smearing on timescales smaller than an integration time), but a square grid with a short integration time will provide a very accurate approximation.

By virtue of CASA's global parameters, we already have project and image world coordinate system parameters set correctly.  

We need to change the mapping parameters to specify a square region a bit larger than the interferometric map.
<source lang="python">
integration        =  "10s"
mapsize            =  "1.3arcmin"
maptype            =  "square"
</source> 

We'll observe on a different day.  This doesn't really matter, but if you choose to simulate two different 12m ALMA configurations and combine them, you will have issues concatenating the datasets if they are simulated on the same day with the same antenna names. So, it's a good habit to change the day.

<source lang="python">
obsmode            = "sd"
sdantlist          = "aca.tp.cfg"
sdant              = 0
refdate            = "2012/12/01"
totaltime          =  "2h"
</source>

Run {{simobserve}}, displaying graphics to screen and to files.

<source lang="python">
simobserve()
</source>

----

====Simulate 7m ACA observation====
[[Image:M51c.aca.i.skymodel.png|thumb|hexagonal map overplotted on sky model]]

Next we'll add an ACA mosaic, with its larger primary beam.

<source lang="python">
integration        =  "10s"
mapsize            =  "1arcmin"
maptype            =  "hex"
pointingspacing    =  "15arcsec"
</source> 

We can specify an integral number of times to repeat the mosaic by setting <tt>totaltime</tt> to an integer string without units.

<source lang="python">
obsmode            = "int"
refdate            = "2012/12/02"
antennalist        =  "aca.i.cfg"
totaltime          =  "3"
</source>

Run {{simobserve}}, displaying graphics to screen and to files.

<source lang="python">
simobserve()
</source>

----

====Deconvolve the visibilities back into an image====

Next we use {{simanalyze}} to combine the three measurement sets and create a single image. 

There are many ways to do this, and you may wish to discuss options with scientists at your ARC. In this example we will use the total power image as a model when deconvolving the ACA image, and then use the result as a model when deconvolving the 12m interferometric image.  This method tends to give low weight to the large spatial scales, but is simple to illustrate.

It's possible to get better results if one used multiscale clean in the tclean task (again using the lower resolution image as a model when deconvolving the higher resolution one).  An alternative would be to create an image independently from each dataset, and then use the CASA feather task to combine them entirely in the image plane.

If given a total power and interferometric measurement set, {{simanalyze}} will automatically create the total power image, 
then use it as a model and deconvolve the interferometric image.

<font color="red">
If you decide to concatenate the two measurement sets and image all visibilities simultaneously, it is critical that the relative weights be set between the two different interferometric arrays. Simulated data has weights=1, since the thermal noise is generated uniformly per baseline. However, in reality the 7m baselines have lower sensitivity than the 12m baselines, and their weights must be decreased by that sensitivity ratio. <tt>simalma</tt> uses the <tt>visweightscale</tt> parameter of <tt>concat</tt> to apply that lower weight of (7./12)**2 to the 7m visibilities. If you wish to combine data manually, you must do this step yourself. (See [[Simalma]] guide.)</font>

====First image total power and ACA with total power as a model====
[[Image:M51c.aca.i.analysis.casa5.4.png|thumb|Total power combined (with relatively low weight) with ACA]]

<source lang="python">
default("simanalyze")
project            =  "m51c"
vis                =  '$project.aca.i.ms,$project.aca.tp.sd.ms'  
imsize             =  [512,512]
cell               =  '0.2arcsec'
analyze            =  True
showpsf            =  False
showresidual       =  False
showconvolved      =  True
simanalyze()
</source>

====Next add the 12m interferometric data====
[[Image:M51c.ALMA_0.5arcsec.analysis.casa5.4.png|thumb|TP+ACA combined (with relatively low weight) with 12m ALMA]]
Here we explicitly have to set the ''modelimage'' to the result from the previous run.

<source lang="python">
default("simanalyze")
project            =  "m51c"
vis                =  '$project.ALMA_0.5arcsec.ms'
imsize             =  [512,512]
cell               =  '0.1arcsec'
modelimage         =  "$project.aca.i.image"
analyze            =  True
showpsf            =  False
showresidual       =  False
showconvolved      =  True
simanalyze()
</source>