Simdata New Users Guide 3.1
Explanation of the guide
This guide is intended to be used as an initial walk-through of how to use simdata. We will start with an image similar to something that might be observed with ALMA, and then we will show how to rescale the image and predict how it will look when observed with different antenna configurations.
We will assume only a general knowledge of interferometry and no specific knowledge of CASA.
The two things you need to get started are:
- The image to work with
- The current version of CASA
To get the image, download the Spitzer IRAC 8 micron image of 30 Doradus from the Simulation Inputs CASA Guide page.
To install CASA, follow the instructions given here. This guide was written for CASA version 3.1
CASA is the post-processing package for ALMA and EVLA and can handle both interferometric and single dish data. Because simdata is a task within CASA, we start here with a brief introduction to some CASA basics. To learn much more about CASA, go to the CASA homepage. Walk-throughs of CASA data reduction for a variety of data sets can be found on the casaguides website.
Once you have installed CASA, you can launch it by typing "casapy" at the prompt or by double-clicking on the icon, depending on your system and preferences.
Running CASA Tasks
To see a list of all available CASA tasks, at the CASA prompt type
To look at the inputs for an available task, use "inp". For instance
> inp simdata
Any parameter with a grey background is expandable. Red text shows an invalid value. Blue text shows an accepted, but not the default, value.
To get help on a given task, use "help". For instance
> help simdata
To reset a task to its default values, use "default". For instance
> default simdata
To run a task, just type its name at the CASA prompt. For instance
Getting Your Input Image Into Simdata
Now tell simdata where to find the model (input) image and how to scale it appropriately for our purposes. Just to be safe, we'll first restore the default values of simdata.
> default simdata > modifymodel = True > skymodel = '30dor.fits'
We are using a Spitzer 8 micron image of 30 Doradus in this example, and we are going to ask simdata to modify this image in some important ways:
- Angular scale
- Observed wavelength
- Brightness scale
If you open the fits image of 30 Doradus in your favorite viewer, you will see that it covers quite a large footprint on the sky, about 10' on a side. We are going to tell simdata to rescale the pixels to shrink the image by roughly a factor of 15 (from 3.6" to 0.25" pixels) so that the model is approximately 40" on a side. This rescaled model will fit within a small mosaic of 6 pointings. Although we do this primarily for convenience in this example, a scientific motivation for this type of rescaling would be to approximate what a super-giant HII region like 30 Doradus would look like if moved from the Large Magellanic Cloud to the distance of M33 or M31. For the sake of demonstration, we will also change the coordinates of the center of the map.
> incell = '0.25arcsec' > indirection = 'J2000 10:00:00 -40:00:00'
The model image of 30 Doradus shows the 8 micron continuum emission. ALMA does not observe at wavelengths this short, so we will tell simdata that this is actually a 230 GHz (1.3 mm) continuum map. We will also tell simdata that the observations were taken with a 2 GHz bandwidth. Although for this particular example the channel width is not a critical number, it would be very important if we were modifying a spectral cube instead of a continuum image.
> incenter = '230GHz' > inwidth = '2GHz'
The 8 micron emission is probably not a great proxy for the millimeter emission from 30 Doradus. For a true science case, one would want to calculate what the expected 230 GHz emission would be from an object like this at the distance of about 750 kiloparsec. For the sake of simplicity, we will rescale the image so that the brightest pixel in the map has a flux density of 0.1 mJy. This number is chosen such that the extended emission is a factor of a few brighter than the expected noise in a 2 hour observation.
> inbright = '0.1mJy/pixel'
Defining the Mock Observations
Now that simdata knows how to interpret the input image, the next step is to define the simulated observations.
Pointings and Scan Time
We will first change the parameters within "setpointings"
The default value for "integration", 10 seconds, might be appropriate to simulate real observations. However, simdata will run much faster if the value for "integration" is increased. In this demonstration will will set it to 600 seconds. When simdata is used for scientific purposes it may be best to set "integration" to a large value at first to make sure that simdata runs as expected, and then decrease "integration" to a more realistic time for the final run.
We will keep "direction" at the default (blank) value to center the observations on the model coordinates, as given in "indirection" above.
We will set mapsize to ' ' (blank) to cover the entire model, which in our case will require a mosaic of 6 pointings (as we will see later on). The mosaic default pattern "maptype" is 'hexagonal', which we will leave unchanged.
Finally, we will change the value of "pointingspacing" to ' ' (blank) to set the pointings to be half a primary beam apart.
> integration = '600s' > mapsize = ' ' > pointingspacing = ' '