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We start with a list of features that the VLBI community thinks are useful for calibration strategies in general:
Amplitude calibration of VLBI data is generally accomplished using measured data from all the antennas, typically system or antenna temperatures, sensitivity estimates (JyK), opacity corrections based on tipping runs and CE estimates of source flux densities (e.g. the phased VLA). There exist at least two fundamental log formats containing this data (e.g. MKIIIMKIV and VLBA). Amplitude calibration tasks capable of processing one or more of these external input formats will be required. (AIPS equiv: ANCAL, ANTABAPCAL).
In many spectral line VLBI projects amplitude calibration can be derived by applying the system temperature and gain for a single element in the network, and comparing the autocorrelation spectra on all elements (AIPS equiv: ACFIT).
Not only for spectral line purposes, but also for high dynamic range continuum VLBI, it is necessary to calibrate the (complex) spectral response for separate BBC channels of each telescope. In some cases one has to rely on autocorrelation data to obtain the amplitude filter shape. In other cases one can solve for phase and amplitude with respect to the average amplitude and phase on each baseline, or derive the bandpass by comparing to a source model (point source, model components or map) (AIPS equiv: BPASS) or by fitting Chebysev polynomials (AIPS equiv: CPASS).
Line VLBI is traditionally done with fixed frequency settings. To do the Doppler tracking, necessary to get optimal spectral sensitivity, resolution and correct labeling, one must shift each cross-spectrum according to an accurate model of the antenna motions and the earth geometry. This task also requires knowledge about the correlator model that was used to take the data. It is required that the user can run this task (AIPS equiv: CVEL) in AIPS++ without the necessity to supply detailed information about the observing conditions.
Spectral line VLBI requires the ability to change the spectral sampling both by simple averaging, as well as by Fourier filtering. Cases where this is needed are for example efficient fringe fitting, optimal detection by matching line width, or combining data with different spectral sampling. It is hoped that AIPS++ will store information about spectral resolution as well as the spectral sampling.
Most VLBI antennas inject tones into the signal path which are extracted downstream to assess the varying electronic delays between IFs. Routines to read, process and apply these corrections to data are needed (AIPS equiv: PCLOD, PCCOR).
Information such as weather information, total-electron content measurements, WVR data, CALCSOLV output etc. may be available in many cases. There is a need for tasks to read all these (section 3.3). General tools will be needed to display, edit and interpolate such data. Furthermore specialized tasks are required to apply the corrections to the VLBI data.
Space VLBI may also require specialized external data. For instance, complicated phase corrections can be imagined to be necessary for intrinsic satellite effects (e.g. delay flutter, frequency variations). These can possibly be derived from external data; often these will depend (in a complicated way) on the position of the spacecraft.
The weaker phase stability of VLBI data makes this the main area requiring additional development compared to any AIPS++ CE data path. Robust and sensitive fringe fitting can in some cases be an important factor for the detection threshold of VLBI. Fringe fitting is a calibration task similar to other AIPS++ calibration requirements, but it is slightly more complex in that it requires the data to be Fourier transformed to delay/rate. Further investigation is required to determine how best to incorporate fringe fitting into the Measurement Set formalism, with particular reference to the separability of individual calibration factors.
As a minimum, the fringe fitting in AIPS++ must have a similar functionality as the combination of FRING and BLING in classic AIPS. BLING offers a baseline-based fringe fit, FRING offers global fringe fitting. The AIPS version has the capability to fit simultaneously for single and multi-band delay or to treat all IF bands separately (e.g. for ``manual phase cal''). This is required for fringe fitting in AIPS++ too, as well as the possibility to average different Stokes data and to fringe fit in rate only for spectral line VLBI.
Fringe fitting should be robust in the absence of some of the IF bands or in the case that some frequencies are flagged (e.g. due to interference). It should offer flexibility in setting the search windows and report clearly about the computational resources requested by the user. Spectral averaging before delay searching is required in order to allow large solution intervals for data sets with many channels. Different solution intervals for delay and rate could be a useful option. It should be possible to display the data in the two dimensional Fourier domain. In many cases the efficiency and sensitivity of fringe fitting could be enhanced by (automatically) limiting the search windows after an initial detection has been made.
Moreover, there are many enhancements requested over the functionality of current AIPS fringe fitting. For detection of phase offsets between hands of different polarization, fringe fitting of cross-hand data is required. For polarization calibration it is also required that the task retains carefully the phase difference between the two hands at the reference antenna.
In order to take full advantage of the sensitivity of global fringe fitting, it would be advantageous for the program to have a-priori knowledge of the sensitivity of the fringe search. With the source model and basic knowledge of the elements involved in the network it should be possible for the algorithm to pick a reference antenna and work out the optimal way to acquire solutions for all elements. The AIPS++ task should allow control over which antenna is second in line when the reference antenna fails to yield solutions. It should be easy to determine the coherence time for individual baselines.
There is a need for implementing incoherent fringe fitting which is used in millimeter VLBI. Fitting second order phase slopes (acceleration) and other complex fringe-fitting routines will be needed for Space VLBI. These include fringe prediction methods that can be used to extrapolate fringe search windows forwards and backwards in time and the possibility of phasing up the ground array separately to improve sensitivity. To overcome the problem for many spectral line projects of inaccurate knowledge of the spacecraft position, a special fringe fitting routine could possibly be anticipated which allows the derivation of the delay from a multi component spectral line cube.
AIPS++ must allow VLBI data to be subsequently exported to geodetic or astrometric packages such as SPRINT and CALCSOLV. This requires full accountability of AIPS++ in order to derive ``totals'' from the correlator model and the result of fringe fitting. Data writers for these formats are required. (AIPS equiv: CL2HFHF2SVHFPRT).
For astrometric developments it must be possible to recalculate the geometric model. It should be possible to replace part of the model with different components (e.g. replace one tropospheric model with another). It should be possible within the AIPS++ environment to derive antenna positions from interferometer data.