Special considerations for analysis and modeling of spectral-line image cubes are discussed in §8.6. Models of galaxy rotation are fit by GAL to images of the predominant velocity (first moment images usually), while the entire data cube may be fit by task CUBIT.
The addition of model data to an image or uv data set is often useful either to simplify later processing steps or to study processing steps using a “source” of known structure. For example, the removal of the response to an appropriate uniform disk from the uv data for a planet will leave Clean the task of deconvolving only the remaining fine-scale structure to which it is well suited. The removal of a few bright point sources of known position and strength may allow imaging with significant tapers in a numerically smaller field. The tasks MODIM, MODSP, UVMOD, MODAB, and SPMOD will add (or subtract) up to 9999 point, Gaussian, disk, rectangular, spherical, or exponential sources to the (scaled) input image or uv data, respectively. These tasks can also add noise and allow the original data to be replaced by the model. UVMOD can include a spectral index for the sources, SPMOD includes spectral lines for the sources, MODIM can include both a spectral index, a rotation measure, and even a rotation measure thickness for each component, and MODSP can include polarized spectral lines with spatially varying spectral structure. Type EXPLAIN MODIM ; EXPLAIN UVMOD ; EXPLAIN SPMOD ; EXPLAIN MODAB ; EXPLAIN MODSP C R for details or see Memos 1187 and 1228 . For simple cases, the older task IMMOD may be easier to use than MODIM.
The task CCMOD will create a clean-components file representing a Gaussian, disk, or Gaussian times disk model. Clean may then be “restarted” with the model as its initial set of components.
The task UVFIT may be useful for fitting Gaussian or uniform-sphere models to uv data sets with a maximum of 2.5 million visibilities. Up to 60 source components may be fit along with up to 30 antenna gains. Provision for getting initial guesses for the specified spectral channel from a text file with guesses for many channels is now available as is a compact text file output option for the solutions. Task OMFIT is a more complicated and difficult to use task which fits source components in a uv data set along with antennas gains. It allows multiple different model types and even a full self-calibration. The results from OMFIT are often well worth the extra effort to obtain them.
contains tasks which create simulated data. PATGN will make images of various patterns. DTSIM generates uv data following instructions given primarily in a keyin-format text file. You may specify the antennas in detail or as the VLBA or VLA. Source parameters and models, frequency values and structure, data calibration errors of numerous types, scan structure and sequence, and more may be specified. UVCON also generates uv data from “scratch” using text files of antenna data and Tsys and efficiency data as well as images of a source model. The two tasks differ in their approach, making both of considerable interest.
Several tasks now perform some analysis on uv data. The other new task, ELFIT, fits polynomials to table data as functions of elevation, zenith angle, hour angle, parallactic angle, or azimuth. It has been used, for example, to measure antenna spillover, plotting Psys versus zenith angle.
DFTIM is similar to the plot task DFTPL in that it Fourier transforms visibility data to a partuclar image pixel (clestial coordinate). DFTPL makes a plot versus time for a single group of frequencies. DFTIM instead writes out an image at that coordinate with frequency and time as the axes. The frequencies and times in this “waterfall” image may be averaged as the image is created. ACIMG makes a similar waterfall plot for autocorrelation data, with the third axis being antenna number.
Statistical analysis of visibility data is performed by several tasks. UVRMS averages the selected data in SOLINT time intervals and then can plot the average values as a function of time and as a histogram. A variety of statistical parameters are computed and printed. SPRMS determines and plots the mean and standard deviation of the selected data as a function of spectral channel. Note that both tasks will combine data from multiple sources, baselines, etc. if so instructed. RIRMS computes statistics over the included visibility data and then prints a matrix over baselines of the real and imaginary parts separately. Histogram plots and plots over time may be generated. In 31DEC21, task VBRFI computes statistics for a data set consisting only of auto-correlation records. It writes out a text file containing the results and can make plots. PLRFI can make plots from one or more of these text files.