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Abstract
Abstract The CMS experiment relies on a tracking detector entirely based on silicon sensors for the reconstruction of charged particles in the hostile radiation environment of proton–proton collisions at the CERN LHC. From the recent data collected by CMS at a center-of-mass energy of 7 TeV, the tracking, vertexing and b-jet identification performance have been measured. These measurements include the muon tracking efficiency, track transverse momentum resolution, track impact parameter resolution, primary vertex efficiency and resolution, and b-jet identification efficiency. The tracking performance relies, in particular, on the excellent pixel detector tracking capabilities, which are exploited in many contexts such as seeding, b-jet identification, and beam-spot monitor. The pixel stand-alone tracking and vertexing capabilities are shown for a particular application: the beam-spot monitor.
Highlights
CMS [1] is one of the two general purpose experiments at the CERN LHC collider [2]
The CMS experiment relies on an all-Silicon based tracking detector for the reconstruction of charged particles in the hostile radiation environment of proton-proton collisions at the CERN LHC collider
In this article are outlined measurements of the tracking, vertexing and b-jet identification performance recently estimated from data measured by CMS at the center of mass energy of 7 TeV
Summary
CMS [1] is one of the two general purpose experiments at the CERN LHC collider [2]. The p-p collisions at LHC, at the unprecedented luminosity of 1034 cm−2 s−1 and center of mass energy of 14 TeV, will produce in the order of 20 superimposed events at a rate of 40 MHz, resulting in thousands of tracks in the acceptance of the tracking system per bunch crossing. To meet the physics requirements of CMS in the innermost, radiation hostile region, the high-precision and low-background tracking is based on a Silicon pixel detector (see Fig. 1 for the tracker layout). The Silicon pixel detector has ∼66 M channels, it covers ∼1.1 m2 of sensor area and it’s divided in three barrel layers, whose the innermost is at ∼4.3 cm of radius, and four endcap disks (two on each side). Pattern recognition: the second reconstruction stage, based on the combinatorial Kalman filter, uses a first estimate of the track parameters, calculated from the seed, to collect the full set of measurements associated to the same charged particle. Starting from the current parameters, the trajectory is extrapolated to the layer of the tracker and compatible hits are selected based on the χ2 between the predicted and the measured positions. In addition to requirements on the number of hits, the χ2 of the fit and the energy, tracks are selected according to their compatibility with the reconstructed vertices
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