The shape of the *Dirty Beam* can be controlled by multiplying
with other weighting functions. Note that the measured visibilities
already carry a weight which is a measure of the signal-to-noise ratio
of each measurement. Since there is no control on this weight while
mapping, it is not explicitly written in any of equations here but is
implicitly used.

Full weighting function as used in practice is given by

(11.2.4) |

As is evident from the plots of -coverage, the density of -tracks decreases away from the origin. If one were to use the local average of the -points in the -plane for mapping as is done in the gridding operation described below, the signal-to-noise ratio of the points would be proportional to the number of -points averaged. Since the density of measured -points is higher for smaller values of and , visibilities for shorter spacings get higher weightage in the visibility data effectively making the array relatively more sensitive to the broader features in the sky. The function controls the weights resulting from non-uniform density of the points in the -plane.

Both and provide some control over the shape of the *Dirty Beam*. is used to weight down the outer edge of the
-coverage to decrease the side-lobes of at the expense
decreasing the spatial resolution. is used to counter the
preferential weight that the -points get closer to the origin at the
expense of degrading the signal-to-noise ratio.

is a smoothly varying function of () and is often used as . For most imaging applications, is a circularly symmetric gaussian. However other forms are also occasionally used.

Two forms of are commonly used. When for all values of (), it is referred to as `natural weighting' were the natural weighting of the -coverage is used as it is. This gives best signal-to-nose ratio and is good when imaging weak compact sources but is undesirable for extended sources where both large scale and small scale features are present.

When where is a measure of the local density of
-points around (), it is referred to as `uniform weighting'
where an attempt is made to assign uniform weights to the entire covered
-plane. In standard data reduction packages available for use
currently (*AIPS, SDE, Miriad*), while re-gridding the
visibilities (discussed below), is equal the number of
-points
within a given cell in the -plane. However it can be shown that
this can result into serious errors, referred to as *catastrophic
gridding error* in some pathological cases. This problem can be
handled to some extend by using better ways of estimating the local
density of -points (Briggs, 1995).

Eq. 11.1.2, using the weighted sampling function is written as