Difference between revisions of "Simalma v2"

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[[Category: Simulations]]
 
[[Category: Simulations]]
  
This guide demonstrates how to use the '''simalma''', a simple task that enables users to simulate observations using the main 12m array combined with the ACA and total power antennas.  To learn how to create a script of the Python code on this page see [[Extracting scripts from these tutorials]].
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'''This link is no longer active.'''
__NOTOC__
 
  
ALMA consists of the main array of 12m antennas plus the ALMA Compact Array, which includes an array of 7m antennas and a separate set of 12m antennas used for Total Power measurements. CASA enables users to simulate observations using any or all of these components.
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You can find a CASA guide for using <tt>simalma</tt> here: [[Simalma_(CASA_4.2)]]
  
== Simulating Observations with the Main 12m Array and the ACA: Manual Combination of the Data ==
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You can find a guide specific to simulating ALMA data here: [[Guide_To_Simulating_ALMA_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'''.
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You can find a guide to simulating interferometric data in CASA version 4.2 here: [[Simulating_Observations_in_CASA_4.2]]
  
This "manual" method of combining main-array data with ACA data is described in the [[ACA_Simulation_(CASA_4.1)]] guide. 
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You can find a guide to simulating interferometric data in CASA version 4.1 here: [[Simulating_Observations_in_CASA_4.1]]
 
 
== The simalma task ==
 
 
 
New in CASA version 4.1 is the '''simalma''' task, which takes one set of parameters describing the region of the sky to observe, and makes the appropriate calls to '''simobserve''' and '''simanalyze'''.  Here we give an example showing how to use '''simalma'''.
 
 
 
<font color="purple">Note: As of July 2013, ALMA is still optimizing the algorithms for combining total power and interferometric data, so the sample parameters used here are likely to change as recommended observing strategies evolve.</font>
 
 
 
=====Set simalma as current task=====
 
Reset all parameters to default, and then set the project name to ''m51''
 
<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"
 
</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 http://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 respecify 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====
 
[[Image:M51.alma_cycle1_3.skymodel.png|thumb|hexagonal mosaic overplotted on sky model]]
 
 
 
We will simulate observations using the main array in "configuration number 3" from Cycle 1.  This configuration affords ~0.5 arcsec resolution.
 
<source lang="python">
 
antennalist="alma_cycle1_3.cfg"
 
</source>
 
 
 
We'll set the 12m array observing time to 2 hours:
 
<source lang="python">
 
totaltime="7800s"
 
</source>
 
 
 
Following the Cycle 1 convention, we will instruct <tt>simalma</tt> to observe 3 times longer with the 7m array and total power dishes.
 
<source lang="python">
 
acaratio=3.0
 
acaconfig="aca_cycle1.cfg"
 
</source>
 
 
 
We set the precipitable water vapor to 0.6 mm to represent observations in nominal weather.  The simulation will add noise to the data based on this setting.
 
<source lang="python">
 
pwv=0.6
 
</source>
 
 
 
To cover all of the 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:
 
<source lang="python">
 
mapsize="1arcmin"
 
</source>
 
 
 
Finally you can check the input settings and run the simulation.
 
 
 
<source lang="python">
 
inp
 
go
 
</source>
 
 
 
=====What does it do?=====
 
 
 
[[Image:M51.alma_cycle1_3.observe.png|thumb|12m observation]]
 
 
 
The 12m array observation is simulated first -- <tt>simalma</tt> simply calls <tt>simobserve</tt> with your input parameters.
 
<tt>simobserve</tt> generates a graphic showing the elevation of the target and the synthesized dirty beam:
 
 
 
The 12m-only visibilities are not currently imaged separately from the 7m visibilities, but this is an expected upgrade in a future release.  One could easily image the generated measurement set, which will be named according to the <tt>antennalist</tt> parameter above -- in this example, it is called <tt>m51.alma_cycle1_3.noisy.ms/</tt>.
 
 
 
<p>
 
----
 
 
 
[[Image:M51.aca_cycle1.skymodel.png|thumb|ACA hex map]]
 
Next, the 7m ACA observation is simulated, with a second call to <tt>simobserve</tt>.
 
<tt>simobserve</tt> follows the same conventions as the ALMA Observation Preparation Tool, and sets the mosaic pointings to cover the area requested.  It takes fewer 7m pointings to cover the region than it did 12m pointings.
 
 
 
It is useful to know that a version of the input sky model convolved to the ACA resolution is generated, in this example <tt>m51.aca_cycle1.skymodel.flat.regrid.conv/</tt>.  That image can be useful to better understand the simulation results.
 
 
----
 
 
 
Next, <tt>simobserve</tt> is called a third time to generate the total power image.  Again according to Cycle 1 conventions, the total power map covers the same region as the main array mosaic, except an extra pointing is 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.
 
[[Image:M51.aca.tp.skymodel.png|thumb|TP map]]
 
 
 
----
 
 
 
====Deconvolve the visibilities back into images====
 
 
 
Next <tt>simalma</tt> uses '''simanalyze''' to combine the three measurement sets and create a single image.
 
 
 
There are many ways to combine data from the separate observations.  If you are dealing with real ALMA data, you may wish to discuss options with scientists at your ARC.  Currently, <tt>simalma</tt> concatenates the two sets of interferometric visibilities first, images them, then separately images the total power observations, and finally uses the <tt>feather</tt> task to combine the two images.
 
 
 
The total power image is generated using gridding tools from the ASAP package inside of CASA.  <tt>simalma</tt> attempts to find the optimal gridding kernel to achieve maximum sensitivity and resolution of the single dish map.  (Finding optimal parameters is an area of active investigation.)
 
 
 
<font color="red">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.  <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.</font>
 
 
 
The concatenated visibilities are imaged, and diagnostic graphics with "concat" in their names are generated:
 
 
 
[[Image:M51.concat.image.png|thumb|combined interferometric map]]
 
 
 
 
 
----
 
 
 
A note for those combining data by hand: When combining the single dish and interferometric maps in the image plane using the <tt>feather</tt> task, one must use the interferometric map <it>without</it> 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, and do not grow artificially.  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.png|thumb|combined maps]]
 
 
 
 
 
 
 
{{Checked 4.1.0}}
 

Latest revision as of 14:37, 23 October 2013


This link is no longer active.

You can find a CASA guide for using simalma here: Simalma_(CASA_4.2)

You can find a guide specific to simulating ALMA data here: Guide_To_Simulating_ALMA_Data

You can find a guide to simulating interferometric data in CASA version 4.2 here: Simulating_Observations_in_CASA_4.2

You can find a guide to simulating interferometric data in CASA version 4.1 here: Simulating_Observations_in_CASA_4.1