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Minttu Uunila
Type of contribution: poster

Baseline dependent weights in VieVS

M. Uunila, Aalto University Mets"ahovi Radio Observatory J. Gipson, NVI Inc., NASA Goddard Space Flight Center H. KrŽasnŽa, Vienna University of Technology

It is well known that in processing VLBI data $chi^{2}$ is usually larger than 1, typically in the range of 4-8. This results from either too small measurement errors or of mismodeling the data. By re-weighting the data, by increasing the errors of the observation, we can make $chi^{2}$ $sim$1. In Solve’s operational solutions baseline dependent weights are always applied. VieVS uses a constant weight, i.e., global weighting. In order to use baseline dependent weights in VieVS we run the least-squares adjustment a second time after calculating the re-weights for each baseline in an observation. Baseline weighting reduces UT1 adjustment significantly. Discrepancy between VieVS and Solve is also reduced.