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deconvolver.mem - Function



Package general
Module deconvolver
Tool deconvolver


Make the mem image


Synopsis
mem(entropy, niter, sigma, targetflux, constrainflux, model, prior, mask, imageplane, async)


Description
Makes a mem image using the Cornwell-Evans algroithm, using either maximum entropy (entropy) or maxmimum emptiness (emptiness). The maximum entropy algorithm is the default. You can restart a MEM deconvolution on an existing model image, but the alpha and beta parameters are not yet saved.

Mask images can be used to restrict where the algorithm puts flux. A prior, or bias, image can provide a priori information to the algorithm and effectively limit the support as well as a mask. The prior image can be constructed by smoothing an existing estimate for the brightness distribution and clipping. Any pixel values below 1e-6 will be clipped to this level, so zero or negative pixels will not cause problems.

Currently, only one Stokes parameter may be deconvolved at a time. Stokes I images can be deconvolved with either maximum entropy or maxmimum emptiness. Stokes Q, U, or V should be deconvolved with maxmimum emptiness, which permits negative pixel values. Joint polarization MEM deconvolution is planned for the future.

The mem entropies possible are:

entropy
The smoothness of the image, relative to some prior (also called default or bias) image is maximized. The functional form of the entropy is H = $ \sum$Iln(I/M), where I is the mem image brightness and M is the prior image. As the prior image is positive definite, the entropy constrains the mem image pixels to be positive, hence only stokes I can be imaged.
emptiness
The number of pixels with absolute value of the flux greater than the noise level is minimized. This treats positive and negative pixel values equally, so it is appropriate for any Stokes image.

This MEM algorithm works in the image plane (ie, is ignorant of visibility data), but performs the convolution by multiplication in the Fourier plane. Not to be confused with this usage of the term "image plane", some problems are "image plane" problems, such as a single dish performing On-The-Fly mapping. Independent noise is added at each integration as the beam sweeps over the object (ie, in the image plane). This can lead to a noise signal at non-physically large spatial frequencies. This non-physical signal can be removed by convolving the residual image with the PSF. Also key to this problem is that the PSF is of finite extent, permitting the deconvolution of nearly the entire dirty image rather than just the inner quarter. These options are accessed by setting imageplane to T.



Arguments

entropy   entropy to use
    Allowed: String:'entropy'|'emptiness'
    Default: 'entropy'
niter   Number of Iterations, set to zero for no MEMing
    Allowed: Int
    Default: 20
sigma   Noise level to try to achieve
    Allowed: String
    Default: '0.001Jy'
targetflux   Total image flux to try to achieve
    Allowed: String
    Default: '1.0Jy'
constrainflux   Use targetflux as a constraint? (or starting flux)
    Allowed: Bool
    Default: F
model   Name of input/output model image
    Allowed: String
prior   Name of prior (default) image used for mem
    Allowed: String
mask   Mask image restricting emission (all pixels 0 or 1)
    Allowed: String
imageplane   Is this an image plane problem (like single dish)?
    Allowed: Bool
    Default: F
async   Run asynchronously in the background?
    Allowed: Bool
    Default: !dowait



Returns
Bool


Example
deco.mem(entropy='entropy', niter=30, sigma=0.01, targetflux=10.0,
model='3C273XC1.mem.image', prior='3C283XC1.prior')





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