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Version 1.9 Build 1367 |
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M.H. Wieringa
1995/07/18
Miriad models the transformation of true sky visibility to observed visibility using the following, `antenna-based' gain function:
gp(t)gp() exp(2i(t)( - )). | (1) |
In addition to these `single IF-channel' effects there are two complex leakage terms, Dp, Dq, which describe the polarization mixing that occurs. Given two ideal, orthogonal polarization signals, Ep, Eq, the measured signals are given by:
= . | (2) |
Two additional corrections Miriad can perform:
A `standard' ATCA observation consists of a short observation of a primary flux calibrator and a sequence of secondary calibrator and program source observations. In Miriad calibration proceeds as follows:
The main difference with classic-AIPS calibration is the determination of the Stokes parameters of the secondary in the solution for the complex gains. This is necessary because generally XX and YY are not good estimates of Stokes I (unlike RR, LL) due to linear polarization of the secondary.
The leakage solution cannot solve for all leakage terms using just a primary with known and secondary with unknown polarization. The absolute leakage, or e.g., the X-leakage on the reference antenna, has to be assumed to be zero. Using an observation of a strongly polarized calibrator with known polarization and good parallactic angle coverage these terms can be solved for. The Stokes V parameter also cannot be solved for.
Presently the Miriad bandpass and leakage solvers allow only a point source in the phase center as the model. In AIPS++ we should generalize this to arbitrary models. The bandpass solver allows a frequency dependent model for the source to be specified (to avoid contaminating the bandpass with the spectral index of the calibrator). Similarly, the polarization leakage solver allows a full Stokes model to be specified. Solving for and applying a time dependent bandpass is difficult in Miriad, in AIPS++ we should provide this capability.
There are two selfcal tasks in Miriad, one assumes there is only a single time variable complex gain per antenna, the other allows two gains (X and Y) to be solved for, either independently or phase coupled. There are provisions to specify selfcal models that vary with frequency, polarization or pointing (mosaicing). Not all these options can be combined at present.
We need to be able to cope with several special cases: the data may consist of only a single polarization (XX), two polarizations (XX,YY) or the full set (XX,XY,YX,YY). Only in the last case can we do polarization leakage correction.
One area where Miriad has a rather simplistic approach compared to classic-AIPS is the association of calibration with data. Only a single version of each table (bandpass, leakage, gain) is supported. Recalibration overwrites the old table. There are no provisions for applying the gains from calibrators selectively to some program sources but not others. The solution for these problems in Miriad is to split the data into different sources or groups and apply calibration to these separately. AIPS has a task, CLCAL, which allows one to specify in detail which calibrator to associate with which program sources. Having used both methods, I tend to favor the Miriad approach as the conceptually easier one. In the case with multiple frequencies and multiple 'calibration groups' selectively redoing the calibration can get rather difficult in AIPS. In AIPS++ we might avoid the copy of the actual data when splitting up the observation and provide 'referencing' datasets to which calibration can be applied. Comments on the merits of these schemes are appreciated.