Robust vertex fitters

✍ Scribed by T. Speer; R. Frühwirth; P. Vanlaer; W. Waltenberger

• Book ID: 103855658
• Publisher: Elsevier Science
• Year: 2006
• Tongue: English
• Weight: 120 KB
• Volume: 566
• Category: Article
• ISSN: 0168-9002
• DOI: 10.1016/j.nima.2006.05.031

✦ Synopsis

While linear 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.