Reducing WSRT data with Miriad
Introduction
This documents provides a very brief instruction on how H i spectral line data observed with the Westerbork Synthesis Radio Telescope (WSRT) can be reduced using Miriad. To understand the different steps in the reduction process the reader should already be familiar with the principles of interferometry and data reduction. For more information about Miriad and the individual Miriad tasks use the command 'help' in your Miriad session or consult the Miriad User's Guide. No responsibility is assumed for the correctness or completeness of the information provided on this page.
1. Read in the data files
2. Flagging of bad data
uvflag vis=<infile> "select=shadow(27)" flagval=flag
...
3. Extract the unflagged records to a new uv file
4. Apply Tsys calibration
5. Remove outliers in the visibility data
6. Add rest frequency to the header
7. Obtain bandpass and gain solutions
(Only for calibrators! Specify flux if not known to Miriad!)
8. Copy the gain and bandpass corrections from one (good) calibrator to all data files
9. Set the calibration interval to 24 hours
10. Apply bandpass and gain corrections to all data files
11. Extract continuum
12. Perform self-calibration
Make a map from the uv file:
Clean this map:
Check if the cleaned map looks okay:
If yes, self-calibrate:
These four steps have to be repeated iteratively to improve the complex gain solutions. For the later steps one should use the latest model for the clean procedure. In addition, the cutoff can slowly be decreased as the model improves. In the self-calibration step the interval can also be decreased down to the original data interval of 1 minute. In the last iteration option=amplitude should be selected to calibrate both the amplitude and phase of the gain.
13. Copy gain solutions to line data
14. Apply gain solutions
15. Subtract continuum from line data
16. Cut off the edges of the spectra
17. Make image cube
18. Clean & restore image cube
restor model=<input_model> beam=<input_beam> map=<input_map> out=<output_file>
Alternatively, the maximum entropy method can be used:
restor model=<input_model> beam=<input_beam> map=<input_map> out=<output_file>