Getting Started

Documentation

Glish

Learn More

Programming

Contact Us

Version 1.9 Build 803

News	FAQ
Search	Home

Examples of VLBI Experiments

To get a feel for the possible data types that VLBI processing has to take into account, we list below possible VLBI observations for which AIPS++ processing andor application development should be possible.

The first part of this list concentrates on data types that we consider part of (future) standard VLBI practice:

• continuum observations in single or dual polarization

• spectral line observations in single or dual polarization

• observations in which polarization, frequency, andor pointing centre may be rapidly switching in time.

• simultaneous observations in multiple frequency bands (e.g. for observing multiple lines simultaneously or multi-frequency synthesis or S/X), with variable numbers of channels within each band

• pulsar-gated data

• mosaiced observations with severalmany pointing centres (e.g. large line sources, gravitational lenses). This must be supported at both the u,v dataset and image dataset level

• polarization data with unequal uv-sampling, where all four polarization parameters are not available simultaneously, as might occur for networks with inhomogeneous polarization sampling or time-switching of the recorded polarization

• combination of data from different observations that have different (but overlapping) spectral sampling

• time-series data of profiles and visibilities (e.g. pulsar data with bin number as a data axis)

• multi-array datasets (e.g. MERLIN+EVN)

• multiple correlations from multi-field centre observations, for which the calibration should be identical (e.g. gravitational lenses)

More specialized dataset types which could be taken into consideration in the design of AIPS++ are:

• space VLBI observations

• observations during which the source changes structure (e.g. SS433)

• cluster-cluster data (e.g. WSRT-VLA multi-antenna VLBI)

• burst sampling for mm-wavelength VLBI

• triple correlation (including the case where one of the visibilities has a different frequency)

• combinations of the above (e.g. pulsar gated data in cluster-cluster mode)

Figure 1: A possible model for data flow for VLBI data in AIPS++. The correlator specific format may contain all the calibration data. Examples of some of these are denoted in this figure by their classic AIPS table two letter codes: e.g. PC for phase cal data, SN for amplitude calibration for example based on state counts, TY for system temperatures, FG for flagging, MC for correlator model components. Standard reduction has a specific VLBI part which involves calibration based on external data and fringe fitting. This allows the Measurement Set to be averaged in frequency and time. Following this, standard imaging and (self)calibration techniques are available to improve the image quality and the model of the source and sky.