Speaker
W. Waltenberger
(HEPHY VIENNA)
Description
State of the art in the field of fitting particle tracks to one
vertex is the Kalman technique. This least-squares (LS) estimator is
known to be ideal in the case of perfect assignment of tracks to
vertices and perfectly known Gaussian errors. Experimental data and
detailed simulations always depart from this perfect model. The
imperfections can be expected to be larger in high luminosity
experiments like at the LHC. In such a context vertex fitting
algorithms will have to be able to deal with mis-associated tracks
and mis-estimated or non-Gaussian track errors. We present a vertex
fitting technique that is insensitive to outlying observations and
mis-estimated track errors, while it retains close-to-optimal
results in the case of perfect data; it adapts to the data. This is
realized by introducing weights that are associated to the tracks
and reflect the compatibility of the tracks with the vertex.
Outliers are no longer simply discarded - as is done in most
of the classical robustification techniques - but rather
downweighted. The algorithm will be presented in detail, and
comparisons with classical methods will be shown in relevant physics
cases.
Primary authors
R. Fruehwirth
(HEPHY Vienna)
W. Waltenberger
(HEPHY VIENNA)
