Bottom left: target visibilities before demixing - the interference of Cas A and Cyg A with the target visibilities is the cause of the bump in the data in the second part of the observation. Bottom right: the contributions of the two A-team sources is gone. This is particularly evident in the second part of the observation.
Removing this contribution is essential to make it possible to properly calibrate the target field. To understand whether demixing is needed for your data, you are suggested to inspect the elevation of the A-team sources during your observation. By combining this information with the angular distance of the A-team from your target, you can have a clear picture of how critical is to apply this algorithm to your data to improve the calibration and imaging of the visibilities.
The old demixer will demix in the same way for the entire observation without taking temporal variations into account. One can define:. Basically it works the same as the old demixer, but for each time window it estimates the data by evaluating a rough model of the A-team sources and target. Using those data it tests which sources have to be demixed, which baselines should be used, and if the target has to be ignored, solved, or deprojected.
In this scheme one can specify:. Following this example, the source models of CygA, CasA, and VirA will be subtracted with the gain solutions calculated for them. The target source model is also used to get better gain solutions for the A-team sources.
If no source model is given for the target, the target direction is projected away when calculating the gains. This should not be done if an A-team source is close to the target. Currently, Science Support is investigating how close it can be.
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If too close, one should specify. Examples of demixing performance on real data are given in Fig. In this way BBS can run faster and can a single image be created from the combined subbands. The first line shows that a wildcarded MS name can be given, so all MeasurementSets with a name matching the pattern will be used. The data of all subbands are combined into a single subband and the meta frequency info will be updated accordingly.
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The third line means that only the cross-correlations of the core and remote stations are selected and written into the output MS. Note this is different from flagging the baselines as shown in the preflagger example.
Input selection means that non-matching baselines are fully omitted, while the preflagger only flags baselines. Note that no further operations are needed, thus no steps are given. However, it is perfectly possible to include any other step. In this case one could use count. It is important to note that subbands to be combined should be consecutive, thus contiguous in frequency. This means that the first and last channels of an MS should not be removed, but flagged instead using the preflagger. The steps are executed in the given order, where the data are piped from one step to the other until all data are processed.
Each step has a name to be used thereafter as a prefix in the keyword names specifying the type and parameters of the step. It is possible to skip leading or trailing channels. It sets flags for invalid data NaN or infinite. Dummy, fully flagged data with correct UVW coordinates will be inserted for missing time slots in the MS.
Missing time slots at the beginning or end of the MS can be detected by giving the correct start and end time. This is particularly useful for the imaging pipeline where BBS requires that the MSs of all sub bands of an observation have the same time slots. When updating an MS, those inserted slots are temporary and not put back into the MS.
When combining multiple MSs into a single one, the names of the input MSs can be given in two ways using the msin argument.
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The directory part of the name cannot be wildcarded though. For example,. It is possible to select baselines to use from the input MS. If a selection is given, all baselines not selected will be omitted from the output.
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Note this is different from the Preflagger where data flags can be set, but always keeps the baselines. Different subbands are stored on different nodes, and it may be necessary to search them all for the required data. The msout step defines the output. The input MS is updated if an emtpy output name is given. Output data should not be written back to the storage disks. You need a so-called cluster description file for this.
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For the curious, the cluster description is a simple ASCII file that should be straightforward to understand. A flagger step shows the percentage of visibilities flagged by that step.
It shows:. It shows percentages per baseline and per channel. The msin step shows the number of visibilities flagged because they contain a NaN or infinite value. It is shown which correlation triggered the flagging, so usually only the first correlation is really counted. A Counter step can be used to count and show the number of flagged visibilities. Such a step can be inserted at any point to show the cumulative number of flagged visibilities. For example, it can be defined as the first and last step to know how many visibilities have been flagged in total by the various steps.
These quality data can be inspected using the aoqplot tool. Normally this is done by the command: module load lofar. DPPP some. Yahoo Food Recipes October 13, View photos. What to Read Next.
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