Dirty image INNAME Image name (name) INCLASS Image name (class) INSEQ 0.0 9999.0 Image name (seq. #) INDISK 0.0 4.0 Image disk drive # Beam image IN2NAME Image name (name) IN2CLASS Image name (class) IN2SEQ 0.0 9999.0 Image name (seq. #) IN2DISK 0.0 4.0 Image disk drive # Deconvolved image OUTNAME Image name (name) OUTCLASS Image name (class) OUTSEQ 0.0 9999.0 Image name (seq. #) OUTDISK 0.0 4.0 Image disk drive # DOTV -1.0 1.0 TV display of residuals? NBOXES 0.0 10.0 Number of boxes BOX 0.0 4096.0 Four coordinates for each box GAIN 0.0 VC loop gain, try 0.5 NITER 0.0 32767.0 Maximum # of iterations PHAT -1.0 1000.0 Stabilized smooth param. Try 0 to 0.05 OPCODE Iteration type. Try 'TL','WT' 'VC','PC' or 'ST'. Prefix with '+' for positivity constraint. APARM Use varies according to algorithm. See explain. BADDISK -1.0 1000.0 Disks to avoid for scratch.

APVC Type: Task Use: APVC deconvolves the dirty beam from the dirty map thus producing a map with the units of brightness (Jy/pixel). Conversion to other units e.g. KELVIN can be done with AXDEFINE. APVC may be restarted if further improvement is desired. This program is slow but should be comparable to APCLN if the map contains many picture elements. The task CONVL may be used to convolve the map to the resolution produced by a CLEAN program. This is a rather experimental and ad hoc task, but in some limiting cases it reduces to the standard CLEAN algorithm (though it would be impractically slow) and to the classical van Cittert iteration. Adverbs: INNAME,INCLASS,INSEQ,INDISK.......Specification of the dirty map. Standard defaults apply. IN2NAME,IN2CLASS,IN2SEQ,IN2DISK...Specification of the dirty beam. Defaults will depend on dirty map spec. OUTNAME,OUTCLASS,OUTSEQ,OUTDISK...Specification of the output deconvolved map, in units of Jy/pixel. Standard defaults, with the output class being 'xVC', 'x' being I,Q,V,U,L or R, depending on the polarization of the dirty map. The algorithm can be restarted by putting the FULL specification of the partially deconvolved map into OUTNAME, etc. In this case, the output will overwrite the partially deconvolved image. DOTV..............................Display each iterration on the TV channel 1. If true, you may stop the iterating with TV button D after each VC map is displayed. Default is no display. NBOXES............................The number of rectangular boxes, as with APCLN. The VC image is non-zero only within these boes. Default is 1. BOX...............................A 4x10 array with the BLC and TRC of each box. 0 implies use one box only, which is the inner quarter of the map. NITER.............................Number of iterations to perform. Default is 20 (usually 15-30 is adequate). GAIN..............................Loop gain. The loop gain is scaled by the volume of the main lobe of the dirty beam, and consequently quite high gains apply (typically 0.2-5). Default is 0.5. PHAT..............................Cornwells smooth stabalizing parameter. Use 0 to 0.1. Default is 0. OPCODE............................This dictates the actual iteration scheme used. Default is 'VC'. Possible values are: 'VC' Van Cittert iteration. 'TL' Cornwells Tunnel iteration. 'ST' Steers CLEAN-like iteration. 'WT' "Weighted" iteration. See EXPLAIN. 'PC' "Percent" iteration. See EXPLAIN. Additionally each of the above can be prefixed by a '+' (e.g. '+VC') which enforces a positivity contraint on the estimate. APARM.............................A hotch-potch of extra parameters which dicate the actual iteration scheme. Sensible defaults apply. See EXPLAIN. BADDISK...........................Bad disks to avoid.

Schemes: ======== The van Cittert iteration consists of the 2 steps: 1) Estimate a correction to add to the current map estimate, by multiplying the residuals by some weight. In the classical van Cittert algorithm this weight is a constant, where as in CLEAN the weight is zero everywhere except at the peak of the residuals. 2) Add the step to the current estimate, and subtract the estimate, convolved with the dirty beam, from the residuals. Though it is a simple algorithm, it works well (if slowly) for cases where the dirty beam is positive semi-definite (as it is in astronomy). The basic idea is that the dirty map is a pretty good estimate of the deconvolved map. The different iterations vary only in the weight to apply to each residual in determining the correction step. Assuming that EST is the map estimate, RES is the residuals, and nppbeam is, roughly, the volume of the central lobe of the dirty beam, then the correction step is given by: gain/nppbeam*RES*WT and WT is: 1. Tunnel Algorithm WT = (offset+EST**2/(EST**2+(resrms/nppbeam)**2)) 2. Van Cittert Algorithm WT = 1 3. Steer ('ST') WT = 0 if RES < resmax*trim = 1 otherwise (NOTE If trim is 1, then Steer degenerates to a slow CLEAN) 4. Percent ('PC') WT = (alpha + (1-alpha)*(RES/resmax)**2)* (RES**2/(RES**2+epsi*resrms**2) 5. Weight ('WT') WT = alpha*(RES/resmax)**2+(1-alpha)*(EST/estmax) If the positivity constraint is being applied, then the correction step is clipped to ensure that the resulting estimate remains positive. Iteration Parameters: ==================== Extra parameters required by each scheme (e.g. alpha, epsi, offset, etc) are given in APARM. Their order and defaults are given below. Defaults are taken in the entered parameter is less than of equal to zero. Variable Entered Scheme Default Typical Nppbeam APARM(1) all Varies 30-50 Alpha APARM(2) WT,PC 0.5 0-1 Offset APARM(2) TL 0 0-0.1 Trim APARM(2) ST 0.8 0.4-1 Epsi APARM(3) PC 4 2-10 Determining Nppbeam: ==================== The Nppbeam parameter (number of points per beam) is a rough estimate of the "gain" of the beam, and this will vary according to the algorithm used and the type of structure in the map (i.e. extended or point-like). The peak of the transform of the beam will be correct when observing a particular sinusoid. Though this estimate is strictly to ensure that ALL (even pathological) cases converge, in practice it is far too conservative. A smaller value is more appropriate. If observing noise, then the rms of the transform of the beam is roughly the gain factor, but this is probably too small an estimate, especially when there is extended structure. When observing point sources, and using the Steer algorithm, a value slightly bigger than 1 is appropriate, whereas when observing structure about the same size as the beam, then the volume of the main lobe is a good estimate. This is approximately given by 1.1331*FWHM*FWHM/CELLSIZE**2. If this parameter is allowed to default, then APVC will calculate the peak and rms of the transformed beam, and take Nppbeam as the geometrical mean of these. Typically when the u-v plane is well sampled, the peak of the transformed beam will be 100-200 and the rms about 5-10, giving a Nppbeam of the order of 30-40.