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The fringe stopping process involves applying virtual signal delays to compensate for varying path delays between tiles during an observation. The major advantage of this approach to geometric corrections is that it is performed prior to cross-correlation, integration and averaging. Prior to fringe stopping, many of the observation modes available to MWA users involving long integration times or broad frequency averaging would have resulted in unacceptable geometric errors, as downstream processing tools require relatively fine-grained data to perform corrections effectively - especially at longer baselines, where the rate of fringe rotation is higher. Short integration times and fine frequency resolution both have a large impact on the size of the visibility data produced in a given observation, without necessarily improving the scientific usefulness of the data - typically, most of this resolution is averaged away in the final data products. Fringe stopping allows users to make use of long integration times and broad frequency averaging, without sacrificing the quality of the data. Smaller data volumes are easier to manage, store and process, and longer observations that may have produced prohibitively large volumes of data in the past may be feasible with fringe stopping.
Diagram: Comparison of geometric corrections process for using Birli and fringe-stopping
We encourage all MWA users to test this new capability for any observations in the first observing semester of 2023. Starting in the second observing semester of 2023, fringe stopping will be the default mode for all MWA observations. Observations without fringe stopping will continue to be supported on an opt-out basis.
Diagram: Comparison of geometric corrections process for using Birli and fringe-stopping
Implementation
A technical summary of the MWAX fringe stopping implementation will be published soon as part of the forthcoming MWAX paper, a detailed description of the design and validation will be published shortly thereafter.
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The validation process will be described in detail in a future publication.
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Diagram: Time-phase plots generated from fringe stopping (left) and Birli (right) data for 20 baselines, observing PicA with an 8s integration time.
Diagram: Time-phase plots for fringe stopping (blue) vs Birli (gold) for five baselines (rows) at increasing integration times (columns, 0.5s, 1s, 2s, 4s, 8s).