Reconstruction of electrons with the Gaussian-sum filter in the CMS tracker at LHC

2003/06/11 by W. Adam, W Adam, R. Frühwirth +5 · 7 citations
Physics and Astronomy · #Neutrino Physics Research #Particle Detector Development and Performance #Particle physics theoretical and experimental studies #physics.data-an

paper · pdf · doi:10.1088/0954-3899/31/9/n01

published as eConf C0303241 (2003) TULT009; J.Phys. G31 (2005) N9 · Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, LaTeX, 14 eps figures. PSN TULT009

arxiv created 2003/06/11 · openalex publication_date 2005/07/25 · arxiv updated 2015/06/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

Abstract

The bremsstrahlung energy loss distribution of electrons propagating in matter is highly non Gaussian. Because the Kalman filter relies solely on Gaussian probability density functions, it might not be an optimal reconstruction algorithm for electron tracks. A Gaussian-sum filter (GSF) algorithm for electron track reconstruction in the CMS tracker has therefore been developed. The basic idea is to model the bremsstrahlung energy loss distribution by a Gaussian mixture rather than a single Gaussian. It is shown that the GSF is able to improve the momentum resolution of electrons compared to the standard Kalman filter. The momentum resolution and the quality of the estimated error are studied with various types of mixture models of the energy loss distribution.

Cited by

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• WZ production cross-section measurement at 7 TeV and 8 TeV center of mass energies within the Compact Muon Solenoid experiment
• Feasibility study of single top-quark and top-quark pair production in association with a Higgs Boson and a Photon at the LHC
• Search for New Physics with Same-Sign Isolated Dilepton Events with Jets and Missing Transverse Energy
• Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC
• Highly boosted dielectron identification in proton-proton collisions at √(s) = 13 TeV

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