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imager.mem - Function

2.3.1 Calculate a deconvolved image with selected mem (maximum entropy) algorithm


Description

Makes a mem image using either the Cornwell-Evans maximum entropy or maximum emptiness algorithms, using the single field or multi-field contexts. The maximum entropy algorithm is the default. The mem is performed on the residual image calculated from the visibility data currently selected. Hence the first step performed in mem is to transform the current model or models (optionally including a componentlist) to fill in the MODEL_DATA column, and then inverse transform the residual visibilities to get a residual image. This residual image is then deconvolved using the corresponding point spread function. This means that the initial model is used as the starting point for the deconvolution. Thus if you want to restart a mem, simply set the model to the model that was previously produced by clean.

Mask images are used to constrain the region that is to be deconvolved. To make mask images, use either boxmask (to define a mask via the corner locations blc and trc) or mask (to define a mask via thresholding an existing image). The default mask is the inner quarter of the image.

The MEM deconvolution only operates on one Stokes parameter at a time. Joint MEM deconvolution for multiple Stokes parameters will be implemented in the future.

Some reference regarding MEM : Cornwell and Evans, Astronomy and Astrophysics (ISSN 0004-6361), vol. 143, no. 1, Feb. 1985, p. 77-83.

Narayan and Nityananda, Annual review of astronomy and astrophysics. Volume 24 (A87-26730 10-90). Palo Alto, CA, Annual Reviews, Inc., 1986, p. 127-170.

The mem algorithms possible are:

Cornwell-Evans Maximum Entropy (entropy)
The classic ”vm” or ”vtess” deconvolution algorithm.
Cornwell-Evans Maximum Emptiness (emptiness)
The historic, but largely undocumented, modification to the Cornwell-Evans algorithm which seeks a model image which is consistent with the data and simultaneously minimizes the number of pixels with no emission (meaning ”with pixel values below the noise level”).
Multi-field Maximum Entropy (mfentropy)
Deconvolution is split into minor and major cycles. For each field, the MEM analog of a Clark Clean minor cycle is performed. In the major cycle, the emission thus modelled is subtracted either from the original visibilities (for multiple fields) or using a convolution (for only one field). The latter is much faster.
Multi-field Maximum Emptiness (mfemptiness)
Just like mfentropy, but with emptiness.

The multi-field mem (mfentropy or mfemptiness) should be used if either of two conditions hold:

  1. Multiple fields are to be deconvolved simultaneously OR
  2. Primary beam correction is enabled. In this case, a mosaiced mem is performed.

Note that for the single pointing algorithms, only a quarter of the image may be deconvolved. If no mask is set, then the deconvolved region defaults to the inner quarter. If a mask larger than a quarter of the image is set, then only the quarter starting at the bottom left corner is used. However, for the multi-field imaging, the entire field may be imaged because the major cycles either do an exact subtraction from the visibilities or because PSF extent is more than twice the extent of the primary beam support.

Before mem can be run, you must run selectvis and defineimage. Before mem can be run with a multi-field algorithm, you should run setvp. You may want to run setmfcontrol before running mem with a multi-field algorithm, though the default control values may be acceptable.

Arguments





Inputs

algorithm

Algorithm to use

allowed:

string

Default:

entropy

emptiness

mfentropy

mfemptiness

entropy

entropy

niter

Number of Iterations

allowed:

int

Default:

20

sigma

Image sigma to try to achieve

allowed:

any

Default:

variant 0.001Jy

targetflux

Target flux for final image

allowed:

any

Default:

variant 1.0Jy

constrainflux

Constrain image to match target flux? else targetflux used only to initialize model

allowed:

bool

Default:

false

displayprogress

Display the progress of the cleaning?

allowed:

bool

Default:

false

model

Names of model images

allowed:

stringArray

Default:

keepfixed

Keep model fixed

allowed:

boolArray

Default:

false

complist

Name of component list

allowed:

string

Default:

prior

Names of mem prior images

allowed:

stringArray

Default:

mask

Names of mask images (0=>no emission, 1=>emission permitted

allowed:

stringArray

Default:

image

Names of restored images

allowed:

stringArray

Default:

residual

Names of residual images

allowed:

stringArray

Default:

async

Run asynchronously in the background?

allowed:

bool

Default:

false

Returns
bool

Example

 
 
im.mem(model=’3C273XC1.mem.model’,  
mask=’3C283XC1.mask’, niter=40, sigma=’0.001Jy’)  
 

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