Full Disk Simulations
EOVSA's 13 antennas provide limited uv coverage for imaging the solar disk, but the baselines do contain considerable flux, especially on shorter baselines. We have good evidence that weak features of the quiet Sun (e.g. filament channels, prominences, and weak plage areas) do show up in the data, but the variations in brightness due to the poorly sampled disk often swamp them. The correct approach to imaging such features is to model the quiet solar disk and remove it from the uv data. then clean the residual flux that contains these weaker features, and finally add the disk model back. Self calibration also requires a good source model that includes the disk.
The purpose of this page is to describe the nuts and bolts of that procedure and document the performance.
NB: Since we are just getting started with this procedure, this page will initially present a number of tests. Ultimately it should become a definitive description of the procedure without a lot of false starts.
Creating Disk Models in CASA
The first step in simulating a source is to create a "sky model," which is an image and associated descriptive header information to represent a source in the sky. CASA's componentlist routines provide a recipe for creating different image components, including gaussian sources, point sources, disks, and so on. I created a python script diskmodel.py that eases the work of creating a disk model, but it remains too simple and will be embellished over time to create a more sophisticated disk.
One can use the data themselves to estimate the size of the disk needed to fit the data. One suggestion is to use the zeros, or nulls, evident in the data. The zeros of the airy disk are listed as:
m = array([ 1.21967, 2.23313, 3.23831, 4.24106, 5.24276, 6.24392, 7.24476, 8.24539, 9.24589, 10.24629, 11.24662, 12.24689, 13.24713, 14.24733, 15.24750])
from which the diameter of the solar disk, in arcsec, is found from the location of the ith minimum, in uv wavelengths as
So the different minima will give different estimates and the final size would have to be some sort of mean of them. In some comparisons with the data, I do find that there is a systematic trend for the diameter to get smaller for larger m. One possibility that I explored is whether a fuzzy disk has different minima than a sharply defined disk, but this does not seem to be the case.