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desired direction. Still, even with box size of one or two channels the estimation of cluster parameter intervals is possible. | ||||||||
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< < | Track finding in a high density environment is rather difficult with silicon pixel and strip detectors, looking at both efficiency and fake rate. Track fitting uses hits associated by the previous track finding phase, thus the proper functioning of this latter is essential. | ||||||||||||||||
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< < | In the present track finding method seeding starts from pixel layers. Seeds are produced by combining pixel clusters from the two innermost pixel layers. If they are compatible a further check is made on the existence of another compatible hit in the third pixel layer. Due to the combinatorical nature of the process, the number of possible combinations grow with track/cluster multiplicity N as N³. | ||||||||||||||||
> > | Finding charged tracks with silicon tracker in a high density environment is rather difficult in terms of efficiency and fake rate. Track fitting uses hits associated by the previous track finding phase, thus the proper functioning of this latter is essential. In the actual track finding method seeding starts from pixels. Seeds are produced by combining pixel hits from the two innermost layers. If they are compatible, a further check is made on the existence of another compatible hit in the third pixel layer. Due to the combinatorical nature of the process, the number of possible combinations grows with track multiplicity N as N2. The incoming charged particle leaves energy, charge in the channels (pixels or strips) of the detector. Neighboring channels are composed to form a cluster. The reconstructed cluster is regarded as a single hit. Up to know only the position of a hit, a kind of center of gravity for the cluster, has been used for track finding and fitting. It turns out that the length, direction and the average deposited energy of the cluster contain valuable information, as well. This report consist of two parts. The first part explains how to extract the parameters of a cluster. They can be easily transformed to track parameters. The second part will discuss the possible applications of the obtained track parameters, such as: | ||||||||||||||||
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< < | Up to know only the position, a kind of center of gravity, of pixel clusters have been used for track finding and fitting. Here new methods for using all information available is presented. These are
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> > | Of course these advantages come at a price: a proper modelling of the
detector is necessary with the use of some powerful tools from numerical
analysis.
At the moment this study deals with barrel clusters only, but
looks at both pixels and strips. The plots
accompanying this study have been made for 1000 minimum bias p+p events
and a single central Pb+Pb event.
Coordinate systemsIn the global coordinate system the z-axis is along the beam direction. Both x- and y-axes are in the bending plane, they can also be decomposed to radial (e.g. pT) and azimuthal components. The electric field E is radial, the magnetic field B is in beam direction, the Lorentz-shift due to ExB is azimuthal. The local coordinate system is attached to detector units. The x-axis is along azimuthal direction, y-axis is parallel with the beam direction, z-axis is radial. The relations of local axes to global directions and fields are summarized in table below.
CapabilitiesThe parameters of a cluster can be transformed to track level. A charged particle at creation point can be described by four geometrical parameters:
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IntroductionTrack finding in a high density environment is rather difficult with silicon pixel and strip detectors, looking at both efficiency and fake rate. Track fitting uses hits associated by the previous track finding phase, thus the proper functioning of this latter is essential. In the present track finding method seeding starts from pixel layers. Seeds are produced by combining pixel clusters from the two innermost pixel layers. If they are compatible a further check is made on the existence of another compatible hit in the third pixel layer. Due to the combinatorical nature of the process, the number of possible combinations grow with track/cluster multiplicity N as N³. Up to know only the position, a kind of center of gravity, of pixel clusters have been used for track finding and fitting. Here new methods for using all information available is presented. These are
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