## AppendixWe must be careful with the interpretation of multi-epoch data,
because analysis methods can strongly affect the reliability of our
results. It is very important to know which features to believe, and
how accurate our measurements of very small changes are. To
investigate these problems we made mapping and modelfitting tests in
which we generated simulated data using the program FAKE in the
Caltech VLBI data analysis package with the same ## A.1. Reliability of features in CLEAN mapsFake data for the 1st and 5th epoch were made using the same 9
gaussian components model, which was fitted to the real 5th epoch data
(see Table 2). These simulated data were each mapped separately
with a point component as a starting model. The final maps had
different extended structures in the northern and southern components,
they also contained apparent bridge emission between these three main
components, which was not part of the model. These detected errors
were presumably due to inadequacies and differences in ## A.2. Estimating modelfitting component position errorsAs described in Sect. 4.1 we can set accurate limits on component
motions by gaussian modelfitting to each epoch. We carried out `fake'
simulations to answer two questions about this procedure. The first
was to determine the size of the remaining random errors due to
different In our first test in order to make the simulation as realistic as
possible we attempted to take account of the fact that the real source
structure in 0710+439 is almost certainly more complex than can be
represented by 9 Gaussian components. This complexity is demonstrated
by the fact the final agreement factors (see Sect. 4.1) of our models
are further from unity than would be expected purely from random
noise. It is conceivable that this extra complexity might interact
with differences in Having chosen a suitable 18 component model we created `fake' data
for the 1st and 5th epochs. Using a procedure as similar as possible
to that used to analyse the real data we then fitted a 9 component
model at each epoch and compared the separations between the gaussians
we obtained. The size of the apparent changes gave us an estimate of
the residual random error. On doing this test we found an apparent
change of A2-C2 separation of 8.09 ## A.3. Biasing due to cross self-calibrationIn our final test we sought to determine if our modelfitting
procedure introduced a systematic error due to initially
self-calibrating all models against the same 5th epoch starting model.
It is possible that real changes might be reduced or removed by
initially trying to force all epochs to agree with the 5th epoch
model. To quantify this effect we simulated the case of a 200
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