View source for Simalma (CASA 5.4) v2

[[Category: Simulations]]

This guide demonstrates how to use '''simalma''', a CASA task that simplifies the process of simulating observations that combine data from the main 12 m array with data from the ALMA Compact Array (ACA).  To learn how to create a script of the Python code on this page see [[Extracting scripts from these tutorials]].
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ALMA consists of the main array of 12m antennas plus the ACA, which includes an array of 7 m antennas and a separate set of 12 m antennas used for Total Power measurements.  CASA enables users to simulate observations using any or all of these components.

== Simulating Observations with the Main 12 m Array and the ACA: Manual Combination of the Data ==

One could simulate observations that use the main array plus the ACA by generating the data for each component separately and then "manually" combining and imaging the data.  The approach is to use '''simobserve''' to generate simulated uv data sets for each component separately, and then combine and image the resulting Measurement Sets using '''simanalyze'''.  This technique is general and can be used to simulate observations using multiple 12m array configurations, as well.  Total power observations can be simulated either in an independent run of '''simobserve''', or integrated with one of the interferometric simulations.  Note that if you simulate total power and interferometric observations simultaneously with '''simobserve''', they must have the same set of pointing centers and the same integration and total time.  These are not realistic conditions.  For example, to reduce edge effects, the Total Power antennas should observe a larger area on the sky than the main array antennas, by about 1/2 of a primary beam.  So, it is generally better to generate the total power data with a separate run of '''simobserve'''.

This "manual" method of combining main-array data with ACA data is described in the [[ACA_Simulation_(CASA_5.4)]] guide.  

== The simalma task ==

The '''simalma''' task has been available in CASA since CASA version 4.1.  This task takes one set of parameters describing the region of the sky to observe, and makes the appropriate calls to '''simobserve''' and '''simanalyze''' for ALMA observations.  Here we give an example showing how to use '''simalma'''. 

=====Set simalma as current task=====
Reset all parameters to default, then set the project name to ''m51'' and allow {{simalma}} to overwrite files
<source lang="python">
# Set simalma to default parameters
default("simalma")
# Our project name will be "m51", and all simulation products will be placed in a subdirectory "m51/"
project="m51"
overwrite=True
</source>

=====Specify sky model image=====
In this example, we'll use an Halpha image of M51 as the model of the sky.   The ''curl'' command used below will copy a data file with the model image to our local disk 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 '''simalma''' will not modify your original input image.  Rather, it will make a copy ''m51/m51.skymodel''.

To make the M51 Halpha image more suitable to a sub-millimeter ALMA observation, we will modify most of the header parameters from the FITS file.  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 than M51. (M51 itself would require a quite large mosaic, and in any case we'd like the angular scale of pixels in the input model to be significantly smaller than the synthesized beam.)
* set the peak brightness to 0.004 Jy/pixel
* set the center observing frequency to 330GHz, and since it's a 2D image we'll set the single "channel" width to be 50MHz.  These parameters are plausible for observing a sub-mm emission line in a galaxy.
<source lang="python">
# Set model image parameters:
indirection="J2000 23h59m59.96s -34d59m59.50s"
incell="0.1arcsec"
inbright="0.004"
incenter="330.076GHz"
inwidth="50MHz"
</source>

----

=====Set up Observing Parameters, and Run=====
[[Image:m51.alma.cycle6.3.skymodel.png|thumb|Hexagonal mosaic overplotted on sky model]]

We will simulate observations using the main array in "configuration number 3" from Cycle 6.  To that we will add the ACA cycle 6 array.  This configuration affords ~0.5 arcsec resolution.  Here is a  [[Antenna_Configurations_Models_in_CASA_Cycle6| list of configuration files available in CASA 5.4]].
<source lang="python">
antennalist=["alma.cycle6.3.cfg","aca.cycle6.cfg"] 
</source>

We will set the total 12m array observing time to 30 minutes. In [[simalma]], the 7m array observing time is set by default to be twice the integration time with the 12m array.  Therefore, if we leave the inputs at the [[simalma]] defaults, the 7m array observations would have a duration of 1 hour. In reality, the time ratios between 12m arrays, 7m arrays, and total power observations vary by array configuration to achieve the correct sensitivity in all arrays/observations. Specific time ratios can be found in Table 7.4 of the ALMA Technical Handbook (see the latest version [https://almascience.nrao.edu/proposing/documents-and-tools/latest/documents-and-tools/ here]). In this case, since we are using the Cycle 6 C43-3 configuration for the 12m observations, Table 7.4 shows that we require a total time for the 7m array observations  that is 2.4 times the 12m observation duration. We then set the 12m array observing time (30 min) together with the 7m time (2.4 x 30 min) as a 2-element array in the <tt>totaltime</tt> parameter:  

<source lang="python">
totaltime=["1800s","4320s"]
</source>

For this simulation we will also include "single-dish" total power (TP) observations with three antennas from the 12m Total Power Array. Again, the total time needed for total power observations depends on the ALMA 12m array, and the time ratio between the total power and the ALMA nominal configurations can be found in the Technical Handbook. While the default integration time in [[simalma]] for TP observations is 4 times the 12m array time, for this particular simulation the factor should be 4.1. We set this using the '''tptime''' parameter. 

In CASA 5.4 simulations, the total power observations are simulated in a single track. So the total time of a TP observations should be roughly 8 hours or less, depending on the target declination, to ensure that the target position remains above the horizon. Until this limitation is addressed in a future CASA release, you should limit simulations that include the TP Array to short total integration times.

<source lang="python">
tpnant = 3
tptime="7380s" 
</source>

We set the precipitable water vapor to 0.9 mm to represent observations in nominal weather (3rd octile) at 330 GHz (you can check this in the ALMA [https://almascience.nrao.edu/proposing/sensitivity-calculator| sensitivity calculator]). The simulation will add noise to the data based on this setting.
<source lang="python">
pwv=0.9
</source>

Because we are simulating a real observation with noise, we will want [[simalma]] to also clean the resulting data. By default, simalma will only produce a dirty image unless we set
a maximum number of clean iterations with the '''niter''' parameter. It is also useful to provide a clean threshold, beyond which no more cleaning is done. For more information on imaging and deconvolution in CASA, please see our [[ALMAguides#General_Imaging_Tutorials | First Look Guides]]. Given the observing parameters in this guide, we set the threshold to 30 mJy, or roughly three times the rms noise in the final image. 

<source lang="python">
niter = 10000
threshold = '30mJy'
</source>

To observe the entire galaxy according to our rescaled pixel size, we'll need a 1 arcmin mosaic, and we'll let <tt>simalma</tt> calculate the pointings for us. We'll also set the final image and cell sizes:
<source lang="python">
mapsize="1arcmin"
cell = '0.1arcsec'
imsize = [600,600]
</source>

Finally you can check the input settings: 
<source lang="python">
dryrun = False
inp
</source>

and run the simulation:
<source lang="python">
go
</source>

=== What does simalma do? ===

[[Image:M51.alma.cycle6.3.observe.png|thumb|12m observation]]

==== Generate visibilities and images for each array ====
The {{simalma}} task first calls {{simobserve}} to simulate the visibilities for each of the three array components: the 12 m Main Array, the 7 m Array, and the 12 m Total Power Array.  The {{simobserve}} task generates a figure showing the elevation of the target, the antenna layout, the uv coverage, and the synthesized dirty beam.

Next {{simalma}} generates an image from each of the three array components, separately.  This step is not essential to getting the final result from the combined arrays, but it provides a useful diagnostic.  The  images will be named according to the <tt>antennalist</tt> parameter above. For example, the ALMA 12m image is called <tt>m51.alma.cycle6.3.noisy.image</tt>.

<p>

[[Image:M51.aca.cycle6.skymodel.png|thumb|ACA hex map]]

It is useful to know that {{simalma}} generates a version of the input sky model convolved to the ACA resolution.  In this example it is called <tt>m51.aca.tp.skymodel.flat.regrid.conv</tt>.  That image can be useful to better understand the simulation results.

Note that the total power map covers the same region as the main array mosaic, with an extra pointing position added around the outside edge of the map so that the total power map is larger than the interferometric mosaic. (Total power maps usually have additional noise and artifacts at their edges).  Furthermore, a square raster pattern is used instead of the hexagonal pattern of the interferometric array maps to better replicate the On-The-Fly mapping method used by ALMA for TP observations. 
[[Image:M51.aca.tp.cycle6.skymodel.png|thumb|TP map]]

==== Combine the UV data from the 3 components and make the final image ====

Next {{simalma}} uses '''simanalyze''' to combine the three measurement sets and create a single image. It accomplishes this in the following manner.

First, {{simalma}} concatenates the two sets of interferometric visibilities, and images them.  Diagnostic graphics with "concat" in their names are generated:

[[Image:M51.concat.cycle6.image.png|thumb|Combined interferometric map ACA + 12m]]

Finally it combines the total power image with the concatenated interferometric image using the CASA task <tt>feather</tt>. The result of this is shown in the combined image at right. Note
that the panel showing the total power image is much larger in angular scale than the others, so the emission appears much more compact at first glance. If you use the CASA {{viewer}}
to explore the various .image files produced by {{simalma}}, you will see that the TP image is on the correct scale as the 12m and 7m images. 

There are many ways to combine data from separate observations.  If you are dealing with real ALMA data, you may wish to discuss options with scientists at your ARC.

==Some notes for combining data "manually"==

# When combining interferometric data from different arrays "manually", it is critical to set the relative data weights properly.  Simulated data have 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 the sensitivity ratio.  {{simalma}} uses the <tt>visweightscale</tt> parameter of {{concat}} 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.
# When combining the single dish and interferometric maps in the image plane using the <tt>feather</tt> task, one must use the interferometric map ''without'' the primary beam correction, and first multiply the total power map by the interferometric sensitivity image (".flux") -- this ensures that noise effects are properly handled on the edges of each map.   After running <tt>feather</tt>, the output is masked to 0.2 times the interferometric primary beam, since the total power map was created larger than the interferometric map on purpose, so the edges of the combined image do not contain any interferometric information.

[[Image:M51.combine.cycle6.png|thumb|Combined map: 12m + 7m feathered with TP]]