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imager.weight - Function

2.3.1 Apply additional weighting to the visibility weights


Description

Apply visibility weighting to correct for the local density of sampling in the uv plane. The imaging weights are calculated on the fly when processing the data and can be viewed by plotweights.

To correct for visibility sampling effects, natural, uniform, radial, and Briggs weighting are supported. These work as follows. Then:

natural
: minimizes the noise in the dirty image. The weight of the i-th sample is set to the inverse variance:
     --1-
wxi = σxi2
(2.4)

where σi is the noise of the i’th sample.

radial
: approximately minimizes rms sidelobes for an east-west synthesis array. The weight of the i-th sample is multiplied by the radial distance from the center of the u,v plane:
         ∘ ---2-----2
wxi = wxi  uxi + vxi
(2.5)

uniform
: For Briggs and uniform weighting, we first grid the inverse variance w_i for all selected data onto a grid of size given by the argument npixels (default to nx) and u,v cell-size given by 2fieldofview where fieldofview is the specified field of view (defaults to the image field of view). This forms the gridded weights W_k. The weight of the i-th sample is then changed:
wxi = -wxi-
      W xk
(2.6)

where W_k is the gridded weight of the relevant cell. It may be shown that this minimizes rms sidelobes over the field of view. By changing the field of view, one may suppress the sidelobes over a region different (usually smaller) than the image size.

briggs: rmode=’norm’
: The weights are changed:
wxi = ---wxi----
      1+ W xkf2
(2.7)

where:

          - R 2
f2 = (5∑*10---)-
      ∑-kW-xk2
         xiwxi
(2.8)

and R is the robust parameter. The scaling of R is such that R = 0 gives a good tradeoff between resolution and sensitivity. R takes value between -2.0 (close to uniform weighting) to 2.0 (close to natural).

briggs: rmode=’abs’
: The weights are changed:
     -------wxi------
wxi = W xk *R2 + 2* σ2R
(2.9)

where R is the robust parameter and σR is the noise parameter.

For more details about Briggs (aka robust) weighting, see the Briggs thesis.

Note that this weighting is not cumulative since the imaging weights are calculated from the specified weight (function of noise; usually 1∕σ2) per visibility (actually stored in the WEIGHT column).

Arguments





Inputs

type

Type of weighting

allowed:

string

Default:

natural

rmode

Mode of briggs weighting

allowed:

string

Default:

norm

abs

none

noise

Noise used in absolute briggs weighting

allowed:

any

Default:

variant 0.0Jy

robust

Parameter in briggs weighting

allowed:

double

Default:

0.0

fieldofview

Field of view for uniform weighting

allowed:

any

Default:

variant 0.0arcsec

npixels

Number of pixels in the u and v directions

allowed:

int

Default:

0

mosaic

Individually weight the fields of a mosaic

allowed:

bool

Default:

false

async

Run asynchronously in the background

allowed:

bool

Default:

false

Returns
bool

Example

 
 
im.weight(type=’briggs’, rmode=’norm’, robust=0.5)  
 
 
Applies Briggs (robust) weighting.  

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