E.10 Continuum imaging hints

The first problem that continuum observers will notice with their EVLA data is that the spectral and time resolution of the data, by default anyway, will be rather more than their science requires. It will be possible to instruct the software which extracts data from the archive to do some averaging in both frequency and time. However, detailed editing for RFI and other issues may require excellent resolution in both these domains. After the data have been edited, you can average data in both domains so long as you are careful not to average so much that you produce radial (bandwidth) and/or transverse (time) smearing within the image area. Note that the increased sensitivity of the EVLA will increase the area over which non-negligible astronomical objects may be found while the wide bandwidth will mean that lowest frequency part of your band will be sensitive, because of its larger primary beam, to a much larger area on the sky than the highest frequency part. The spectral averaging can be done with SPLIT; use APARM(1)=1 and set NCHAV, CHINC, and perhaps SMOOTH appropriately. Similarly, AVSPC can be used with AVOPTION=’SUBS, setting CHANNEL and SMOOTH suitably. You will almost certainly wish to retain some spectral separation, so do not use the “channel 0” option.

Time averaging should be done with UVAVG:


to reset all adverbs and choose the task.


to select the calibrated target data set on disk Sn and catalog number Sm.

> YINC Δt  C R

to average to Δt seconds.


to have all samples in a given interval written with the same time; this helps with self-cal. Other options are available.

> GO  C R

to produce the averaged data set.

UBAVG will do a more aggressive averaging, using baseline-dependent time intervals appropriate for the desired field of view. Do not use UBAVG if you are planning to use self-calibration since it destroys the time regularity in the data on which CALIB depends. IMAGR may now do this extra averaging for you on the fly to reduce the size of the work file it uses. Set IM2PARM(11) and (12).

Imaging of the continuum is discussed at great length in Chapter 5 and those details will not be repeated here. Bandwidth-synthesis imaging, which will be the only form of continuum imaging with the EVLA, will make certain adverbs more important. Set BCHAN and ECHAN to avoid the noisier edge channels. Set NCHAV = ECHAN - BCHAN + 1 and CHINC = NCHAV. This will then image all of your IFs and spectral channels into a single image, positioning each channel correctly in the uv plane. With the EVLA, you will be imaging a wider field of view than you did with the VLA. Use SETFC with IMSIZE 0 ; CELLSIZE 0 to see if you should image with a single facet or with multiple facets. If using multiple facets and trying for significant dynamic range, start imaging with OVERLAP 2 ; ONEBEAM -1, but consider OVRSWTCH = -0.05 or so to switch into faster methods of Cleaning when the dynamic range in the residual is small enough.

It has been widely noted that the noise in the outer channels of each IF (spectral window) is higher than in the more central channels. You may wish to experiment with down-weighting channels by a function of the bandpass correction amplitude. In 31DEC13, task BPWGT offers several options for doing just this, using WEIGHTIT to control what exponent of the bandpass amplitude is used in the correction. A solution, which should be better, is availble in 31DEC14 with task BPWAY. This task operates on ether a scan-by-scan or a source-by-source basis to evaluate the rms as a function of time on each channel individually. It determines the rms on short intervals, like RFLAG, normalizes them over each spectral window, and then smooths the normalized rmses over time functions which can be quite long. The results are then applied to the data to modify the weights on a channel-by-channel, baseline-by-baseline basis. This process should allow data from the edges of the spectral windows to be used appropriately, especially in bandwidth synthesis. Note however, that channel 1 in each window includes signal from both that channel and from channel Nchan + 1 (due to aliasing) and so is probably best ignored.

IMAGR allows you to request automatic finding of the Clean boxes (IM2PARM of 1 through 6). In cases with low sidelobes, this works rather well, but you should probably keep an eye on what it does with DOTV 1 in any case. IM2PARM(12) controls the baseline-dependent time averaging while specifying the maximum field of view you expect. This allows you to reduce the size of the work file considerably which will at least reduce the time required for many of the steps in the imaging proportionally. It may be rather better than that if the work file is very large otherwise, requiring actual reading of the disk every time the data are accessed. Note, however, that the uniform weighting of your data will be affected. This averaging reduces the number of samples at short spacings disproportionally and so appears to reduce their weight in the imaging. Some UVTAPER could be used to compensate for this.

By default, bandwidth synthesis imaging assumes that the primary beam and all continuum sources are the same at every frequency. In fact, the primary beam size varies linearly with frequency (to first order anyway) and sources have spectral index. IMAGR will allow you to compensate for the average spectral index at almost no cost with IMAGRPRM(2). A far more accurate and expensive correction for spectral index may be made if you do the following. First image each spectral channel (or group of closely-spaced channels) separately. Combine them into a cube with FQUBE, transpose the cube with TRANS, and solve for spectral index images with SPIXR. To use these images, set IMAGRPRM(17) to a radius (> 0) in pixels of a smoothing area and put the image name parameters in the 3rd and 4th input image names. Note that this algorithm is expensive, but that it can be sped up with judicious use of the FQTOL parameter. The change of primary beam with frequency may be corrected by setting IMAGRPRM(1) = 25 for the diameter of the EVLA dishes. Note that this algorithm is expensive, but that it can be sped up with judicious use of the FQTOL parameter. These two corrections work together, so that doing both costs very little more than doing just one of them.

A RUN file and procedure named OOCAL has been written to do self-calibration using the spectral-index image(s) and primary-beam correction in the manner used in IMAGR. The procedure runs OOSUB to produce a divided data set, then CALIB to solve for complex gains, and finally TACOP to move the SN table to the real input data set. If your model is a large image rather than Clean components, the procedure IMSCAL in this RUN file will convert the model image into faceted Clean components and then run OOCAL. Similar procedures for FRING, called OOFRING and IMFRING are also available.

If you are observing a strong source and trying for very high dynamic range, you may have to correct for errors that are baseline- rather than antenna-dependent. One source of these errors is the antenna polarization leakage which affects the parallel-hand visibilities in a non-closing fashion. Task BLCAL can be used after you have as good an image as you can get without it. This task will divide the data by the model and average over a user-specified time to find baseline-dependent corrections which may then be applied to the data by setting adverb BLVER. We recommend that you average the divided data over all of the times in your data to get a single correction for each baseline (and IF and polarization). If you use shorter intervals, you run the risk of forcing your data to look too much like your model. Since the polarization leakage is probably a function of frequency, an experimental version of BLCAL called BLCHN has been released. It determines the same correction but does not average over channels. BLCHN can even adjust the resulting correction table to correct for spectral index prior to applying and storing the values. The correction is saved in a table which POSSM and BPLOT are able to display. However, the calibration routines do not know how to apply this table, so BLCHN writes out the corrected data as well as the table. A new task called BDAPL has been written to apply this BD table to another data set.

31dEC16 contains a new TV-menu driven editing task called UFLAG.2 If your image shows stripes, indicative of residual bad data, this task allows you to view the data as it is gridded for imaging and to flag bad cells in the uv plane or even some, but not all, of the individual visibilities contributing to apparently bad cells.