|BIT Value||Decimal Value||Constraint|
|bit 0||1||PSF Band Limit|
|bit 1||2||Mean PSF|
|bit 2||4||Not implemented|
|bit 3||8||Fixed object or PSF (w/bit 0)|
|bit 4||16||Sky Fitting
(Sky is ALWAYS subtracted)
PSF Band Limit - bit 0
This flag determines if the band-limit constraint is used with the PSF. This constraint penalizes the solution if there are significant values at spatial frequencies of the PSF greater than the telescope diffraction-limit. It is recommended that this flag be set at all times as it prevents the PSF from converging to a delta function. An elliptical PSF is allowed but currently only if it is aligned along the x and y-axes. The cut-off frequency values have to be set in the FITS header of conv. The keywords are R0X and R0Y and typically are set the the same. The following is an example of a FITS header having these values set where these define the radius of the cut-off-frequency in pixels. PA allows the position angle of an elliptical support region to be specified.
R0X = 64
R0Y = 64
PA = 0
These values can be determined from the mean Fourier modulus (the square-root of the power spectrum) of the convolution images or measured PSF's. Note that the power spectrum can be easily computed using the powerspec tool in the STSDAS fourier package.
Mean PSF - bit 1
This option penalizes the solution if the mean PSF estimate departs from a mean value. This mean value could be an estimate of the PSF obtained from a reference source or even a star in the field. It could even be a model of the PSF. When this flag is set, the algorithm expects to see a file with the same name as the initial PSF estimate but with the extension "_saa", i.e. psf_saa. This option is useful to use when the user believes that they have a good estimate of the PSF but not good enough for a standard known PSF deconvolution. The algorithm can be allowed to reach convergence with this flag set and then can be restarted with it switched off to permit further departures of the individual PSF estimates. psf_saa is of size N x N
The contribution of the Mean PSF constraint to the total error metric can be weighted between 0 and 1 depending upon the user's confidence in the mean PSF. A value of 0 indicates no confidence and mean PSF error metric will have no effect on the total error metric. A value of unity indicates full confidence. This weighting term appears as the keyword ALPHA in the FITS header of conv, the default value is ALPHA = 1.
Fixed Object or PSF - bit 3
This option permits the algorithm to either fix the object or the PSF to perform a standard deconvolution. As to which is fixed depends on whether the PSF Band limit is also set. To fix the PSF, both the PSF Band limit and Fixed options must be set, i.e. control = 9. To fix the object, the PSF Band limit option must be off so that control = 8.
Sky Option - bit 4
The algorithm can also estimate a mean sky background for each of the input convolution frames. This is especially useful when dealing with very noisy data as the inherent non-negativity constraint of the algorithm produces a half-wave rectification of the noise. When this flag is set, the algorithm uses the FITS image of size NxN called sky having a value equal to estimated the value of the mean sky for the data. An observed sky (flat-field) could be appropriate for this.
Note: If bit 4 is not set, sky is simply
subtracted (no fitting occurs). A sky file (sky) with all pixels
set to zero must ALWAYS be provided when no sky fitting is required, as
sky is always subtracted.
(Note: Bad pixel rejection via conv_sup is NOT supported in Fourier domain.)
If the corresponding Wiener filter is not available, you must provide a default file of size N x N (conv_wf) = 1.0.
For most data, the noise can be modeled by a zero-mean Gaussian characterized by a specific rms value, i.e standard deviation. This can be estimated from empty regions from the data frames or from corresponding sky or dark frames. This noise value needs to be added to the FITS header of the convolution file so that the algorithm knows when to stop. The keyword for this value is N0. This assumes that all of the frames have the same SNR which is typically realistic in that frames with the same exposure time will have the same noise statistics and also the same total power.