Hanning smoothing of uv data (hanningsmooth)

For strong spectral line sources (like RFI sources), the Gibbs phenomenon may cause ringing across the frequency channels of an observation. This is called the Gibbs phenomenon and a proven remedy is the Hanning smoothing algorithm. Hanning smoothing is a running mean across the spectral axis with a triangle as a smoothing kernel. The central channel is weighted by 0.5 and the two adjacent channels by 0.25 to preserve the flux; mathematically this is an N=5 sample Hann window kernel (including the outer-most zero-weight samples in the window). Hanning smoothing significantly reduces Gibbs ringing at the price of a factor of two in spectral resolution. Users should also be aware that it will introduce noise correlations between channels which can affect the interpretation of cross-channel chi-squared or uv-model fitting analyses.

The new hanningsmooth task (based on mstransform) has slightly changed in CASA 4.6. The main difference with oldhanningsmooth, the previous hanningsmooth implementation, is that the new task no longer writes to the input MS, but it always creates an output MS. The new task can also handle a Multi-MS and process it in parallel (see more information here).

In CASA, the hanningsmooth task will apply Hanning smoothing to a spectral line uv data MeasurementSet. The inputs are:

The datacolumn parameter determines which of the data columns is to be Hanning smoothed: ’all’, ’model’, ’corrected’, ’data’, ’float_data’ or ’lag_data’. ’all’ will use whichever of the visibility data columns that are present in the input MS. If ’corrected’ is specified, the task will smooth the input CORRECTED_DATA column and save the smoothed data in DATA of the output MS.

The Hanning smoothing transformation in mstransform is available via a single parameter, as shown below: