2.3.1.0 imager.weight

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 written into a Table column called IMAGING_WEIGHT, which may be plotted using plotweights. In addition this columns may be accessed directly using either the table or ms modules.

To correct for visibility sampling effects, natural, uniform (the default), 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:

wi = 12- σi

(2.4)

where σ_i 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:

∘ ------- wi = wi u2i + v2i

(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 2∕fieldofview 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:

-wi wi = Wk

(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:

w wi = ----i--2- 1+ Wkf

(2.7)

where:

- R 2 f2 = (5*∑10---)- ∑-kWk2 iwi

(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:

------wi------- wi = Wk *R2 + 2*σ2R

(2.9)

where R is the robust parameter and σ_R 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 sigma (expected noise) per visibility (actually stored in the SIGMA column).

Arguments


Inputs
type	Type of weighting
	allowed:	string
	Default:	uniform natural briggs radial 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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