Simulating Observations in CASA v3.0.1: Difference between revisions

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6. [[Deconvolve Image]] (Optional) Go back from the calculated visibilities to a synthesis image
6. [[Deconvolve Image]] (Optional) Go back from the calculated visibilities to a synthesis image


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<big>'''simdata2'''</big><br />  
<big>'''simdata2'''</big> <font color="red">Only works in CASA 3.0.2</font> [https://safe.nrao.edu/wiki/bin/view/Software/ObtainingCASA Obtaining CASA]<br />  


1. [[Getting Started in CASA#Installing CASA | Install CASA]]
1. [[Getting Started in CASA#Installing CASA | Install CASA]]
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7. [[Analyze]] Calculate and display the difference between output and input, and fidelity image.
7. [[Analyze]] Calculate and display the difference between output and input, and fidelity image.
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== Tutorials, Recipes, and Example images ==
== Tutorials, Recipes, and Example images ==

Revision as of 09:16, 8 June 2010


Introduction

Simulation capability in CASA follows the usual two-layered structure: there is a beginner-level python task interface called simdata, which calls methods in the sm C++ tool. The task interface turns a model of the sky (2 to 4 dimensions including frequency and Stokes) into the visibilities that would be measured with ALMA, (E)VLA, CARMA, SMA, ATCA, PdB, etc. The task also can produce a cleaned image of the model visibilities, compare that image with your input convolved with the synthesized beam, and calculate a fidelity image. simdata can add thermal noise (from receiver, atmosphere, and ground) to the visibilities.

The sm tool has methods that can be used to add phase delay variations, gain fluctuations and drift, cross-polarization, and (coming soon) bandpass and pointing errors to your simulated data. sm also has more flexibility in adding thermal noise than simdata, for example for new observatories that are unknown to simdata.

New for 3.0.2: Two task interfaces will be present, the old simdata and a new simdata2. We recommend using simdata2 -- simdata is being retained because it has had more extensive testing, and simdata2 is very new, but in the future simdata will be removed.


CASA simulation uses the aatm atmospheric model, a thin wrapper of Juan Pardo's ATM library, to accurately calculate all atmospheric corruption terms (noise, phase delay) accurately as a function of frequency and site characteristics.

Part of CASA's simulation routines are generic ephemeris and geodesy calculations available in python - see simutil.py.

Because simdata is still actively being developed, documentation may lag reality, please email rindebet at nrao.edu with any questions - It's my job to help you use this software. In particular, you may find that some of the presentations and graphics below show parameter inputs that are slightly different from the latest version of CASA.

Steps to simulation

simdata

pdf presentation explaining the same things as below: File:Tutorial.dec2009.pdf

1. Install CASA

simdata inputs look like this (v3.0.2; click to enlarge): Simdata.png

the links below describe the various sections of inputs

2. Input Model - Preparing a patch of sky for simdata to pseudoobserve.

3. Antenna List - how to specify the positions and diameters of your antennas or stations.

4. Specifying Observation - how to set up what/when/how you want to observe, and the output image details

5. Corrupting Observation - (Optional) For added realism, corrupt your visibilities with thermal and phase noise.

6. Deconvolve Image (Optional) Go back from the calculated visibilities to a synthesis image



simdata2 Only works in CASA 3.0.2 Obtaining CASA

1. Install CASA

simdata2 inputs look like this (v3.0.2; click to enlarge): Simdata2.png

The subtasks are modular i.e. as long as you follow a few conventions about filenames, you can run each bit independently and optionally. For example, you can modify the sky model, then predict ACA visibilities, then run again and predict ATCA 12m visibilities and image and analyze both measurement sets together. You can run once to predict, run interactive clean yourself, and as long as you called your image $project.image, run simdata2 just to calculate a difference image and analyze the results.

2. Modify Model - relabel (scale) the spectral and spatial coordinates and brightness of the sky model image.

3. Set Pointings - calculate a mosaic of pointings and save in a text file. You could also make the text file yourself.

4. Predict - Calculate visibilities for a specified array on a specified day

5. Corrupting Observation - Corrupt the measurement set with thermal noise, phase noise, cross-polarization, etc.

6. Image A subset of clean to re-image the visibilities

7. Analyze Calculate and display the difference between output and input, and fidelity image.

Tutorials, Recipes, and Example images

Simulated ALMA Observation of M51 at z = 0.1 and z = 0.3: fully annotated tutorial

This uses a BIMA-SONG cube of a nearby galaxy and scales it to greater distance.

M51thumb.png
simdata page    simdata2 page
Protoplanetary Disk: sky model and lightly annotated script

This uses a theoretical model of dust continuum from Sebastian Wolff, scaled to the distance of a nearby star.

Psimthumb.png
simdata page    simdata2 page
Nearby edge-on spiral galaxy: sky model, script, and discussion

This uses a Galactic CO cube from the Galactic Ring Survey and places it at 10Mpc, similar to what NGC891 would look like if it were observable from the southern hemisphere.

N891thumb.png
[simdata script]    simdata2 page
Other example input images
Other example output simulations (scripts to reproduce these are coming)



Technical and Planning

I always welcome input on developing the CASA simulator, and these links are meetings, technical documents, and planning discussions. Much of it won't make sense to a new user of CASA::simdata, but may be of interest to those wanting to delve deeper:

  • Simulation Library This will become a library of use cases and examples illustrating different science and observation setups. It is in early stages as of Jan 2010, and we're actively seeking volunteers to turn their simulation projects into use cases.
  • Jan 2010 workshop Including slides and discussion of how simdata and Simulator work "under the hood" and plans for development