Measurement of the Z boson cross-section in - Harvard University ...

Chapter 4: Data Collection and Event Reconstruction 98
Inside-out track reconstruction
Inside-out tracking starts with the silicon detectors, namely the Pixel and the
SCT (see Chapter 2). This technique takes advantage of the very high granularity
of the silicon detectors, which is especially useful at high track multiplicities. In
the first stage, the raw data from the silicon layers is converted into clusters, and
the TDC output from the TRT into calibrated drift circles. The silicon clusters are
transformed into 3-dimensional space-points. In the SCT, cluster information from
opposite sides of a module is combined to form a 3D space-point.Pixel clusters are
obviously 3-dimensional.
The second stage involves track-finding, in which the algorithm searches for prompt
tracks coming from the interaction region. First, track seeds are formed by combining
three space-points, each space-point originating in a unique Pixel or SCT layer. Fig-
ure 4.2 shows the number of track seeds per event in data and in Monte Carlo. The
seeds are extended to the remaining silicon layers to form track candidates, which
are fitted using a combinatorial Kalman fitter/smoothing technique [90]. Outlying
clusters are removed based on their large contribution to the fit χ 2 .
The third stage is termed ambiguity-resolving. Some track candidates share clus-
ters, or are fakes resulting from random cluster combinations. In order to resolve
cluster-to-track association ambiguities and reject fake tracks, each candidate track
is scored, the score indicating the likelihood that the track reproduces the path of
an actual charged particle 2 . The highest scoring track candidates are refitted, shared
2 Track scoring takes into account the number of silicon clusters associated with a track candidate,
the number of shared clusters and the number of holes, i.e. , layers where a cluster is expected but
none is found.