Robust Vertex Fitters

While linear least-square estimators are optimal when the model is linear and all random noise is Gaussian, they are very sensitive to outlying tracks. Non-linear vertex reconstruction algorithms offer a higher degree of robustness against such outliers Two of the algorithms presented, the Adaptive filter and the Trimmed Kalman filter are able to down-weight or discard these outlying tracks, while a third, the Gaussian-sum filter, offers a better treatment of non-Gaussian distributions of track parameter errors when these are modelled by Gaussian mixtures.