Sensitivity of Robust Vertex Fitting Algorithms

Robust vertex fitting algorithms are expected to improve the knowledge of the vertex position and of its uncertainty in the presence of mis-measured or mis-associated tracks. Such contaminations are likely to happen in real data as well as in realistic detector simulations.This note describes a simulation study of the sensitivity of two types of robust algorithms: a trimmed least-squares estimator and an adaptive estimator. The statistical properties of the algorithms are studied as a function of the source and the level of contamination, and compared to the results obtained with classical least-squares estimators. Two typical event topologies are studied, one resembling a high multiplicity primary vertex with a possible contamination from a nearby vertex, and one resembling a low multiplicity secondary vertex in a jet.