Introduction

Fringe stopping is a technique for implementing geometric corrections for an observation in the correlator, prior to cross-correlation , integration and averaging. It is an alternative to performing geometric corrections later in the data processing pipeline, with tools such as Birli or cotter, as has been necessary for MWA observations in the past. In most cases fringe stopping is expected to be superior to traditional post-correlation geometric corrections, and it will be the default mode for MWA observations starting in the second half of 2023. It is currently available for evaluation by request, and feedback allows longer visibility integration times without introducing de-correlation, which in turn allows the size of the correlator output products to be substantially reduced.  The new correlator for the MWA, "MWAX", was designed from the outset to include pre-correlation fringe stopping.  However, it was not supported in the first operational release and users were required to perform phase corrections post-correlation within Birli.  Fringe stopping is currently available for all observations by request and its use is encouraged. Feedback is especially welcome at this stage in the commissioning process.  

Background

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.

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