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Subsections

• General Requirements
• Quantities
• Version Typing
• Multi-File Datasets
• Inhomogeneous Arrays
• Variable Integration Times
• Redundant Data

Data structure

General Requirements

First we list the some general data system requirements. These are probably already included in the infrastructure development for CE data in AIPS++ and required for VLBI.

• the data system file format(s) should be accessible from all supported machines without conversion
• simultaneous sub-netting should be supported
• coordinate handling must be very general and allow different coordinate systems and ephemeris information to be carried along
• support for data with non-regular increments in coordinate axes is required (e.g. non linear velocity axis)
• near field imaging should be supported

• it will be necessary to merge data sets
• support for data errors should be fundamental to the data system, providing a basis for support of data error handling in a variety of applications:
  • easy generation of error models

  • error propagation through a series of tasks

  • error images associated with astronomical images
  • plotting error bars on spectra and profiles

  • automatic warning if contour levels are below noise

  • error-based blanking in display of results

  • properly formatted errors when data are extracted for tabulation

• a processing history should be maintained for each data set that can be reviewed by the astronomer, using an easy to use browser, which allows sorting and editing
• the user should have access to both data values and the ``header'' values that govern the interpretation of the data.

Quantities

All quantities such as antenna position, sky position, time, earth orientation parameters, delay and rate should have sufficient precision for VLBI purposes (including Space VLBI), requiring a double in most cases. There should also be ID strings attached to them to specify the coordinate system they refer to. It will be insufficient, for example, to demand that all AIPS++ antenna positions will be IERS XYZs. The VLBA uses USNO terrestrialcelestial frames and EOPs, while the JIVE correlator is likely to use IERS frames. These differences are important for VLBI observing, particularly for astrometricgeodetic experiments.

For Space VLBI it is necessary that AIPS++ supports the motion of an orbiting antenna.

It would be advantageous (and possibly required for use at some correlators) that AIPS++ allows data to be stored in the lag domain.

Version Typing

All programs should enter andor pass complete version information concerning any (correlation) model applied to the data (e.g. geometric, troposphericionospheric, source structure) to enable complete reconstruction of the total delays measured. Ideally, it should be possible to update a specific component in the model (e.g. take out and replace the geometrical model).

Multi-File Datasets

The short integration times and abundant channelization required for VLBI data (due to weak phase stability) leads to large datasets, particularly for spectral-line experiments. The ability to transparently address large datasets spread across many disks or tapes is of growing importance for VLBI data reduction, and should be implemented in AIPS++.

The data system should support the merging of correlation data and calibration data from different correlators.

Inhomogeneous Arrays

In the calibration (and imaging) steps it should be possible to treat inhomogeneous arrays in which not only antenna characteristics vary widely (e.g. antenna size, system temperature), but also feed specifications are quite different (e.g. linear or circular feeds and equatorial or alt-azimuth mounts). Moreover, calibration procedures can differ between different telescopes (e.g. single dish vs. phased arrays).

Variable Integration Times

In the case of space VLBI or wide field imaging, the ability to have different integration times on different baselines will be needed. This should be possible in the AIPS++ table system.

Redundant Data

Some correlators produce redundant data when particular baselines are correlated more than once. Robust handling of these cases is desired. Some users will choose to resolve these redundancies at the data loading stage, but there are many advantages to allow these redundancies to be incorporated in the data structure and to be resolved during processing. This would also allow AIPS++ to be used at these correlators to manage their data products.