13 Pattern Recognition and Reconstruction
601
of about 230 μm and allowing the reconstruction of the hit position in 3-D. In the
outer layers the sensors are single-sided, and the z resolution can be approximated
by the strip length over
√
12, or about 15 mm. In order to maintain excellent tracking
performance until the Long Shutdown 3 of LHC, the Pixel Tracker was replaced in
the year-end technical stop of 2016/2017 with a new Pixel Tracker composed of four
barrel layers and six forward disks providing four-hit pixel coverage up to |η| = 2.5.
After the Long Shutdown 3, the High Luminosity phase of the LHC (HL-LHC)
is scheduled where the accelerator will provide an unprecedented instantaneous
luminosity of 5 − 7.5 × 10 34 cm −2 s −1 . In [155] the new CMS silicon tracker and its
tracking and vertexing performance for different event types, pileup scenarios and
detector geometries are presented.
The CMS track reconstruction algorithm is based on an iterative approach [156].
The main idea is to search for easier-to-find tracks first, to mask the hits associated to
the found tracks, and to proceed to the next iteration. In this way the combinatorial
problem is reduced, and the search for more difficult classes of tracks is simplified.
Moreover, this approach introduces the possibility of developing special iterations
that can improve track reconstruction in high-density environments such as jets,
or to use the information from other subsystems such as muon chambers and
calorimeters. In each iteration, the Combinatorial Track Finder is run. It can be
divided into four different steps:
1. Seed generation: Using the information of three or four hits, the trajectory
parameters and the corresponding uncertainties of the initial track candidates are
computed.
2. Track finding: Staring from the seed, the current trajectory parameters and
their uncertainties are extrapolated to the next layer and compatible hits are
found. Each of them is added to a clone of the track candidate. Each of these
candidates is again extrapolated to the next layer and compatible hits are found.
This procedure is repeated for each candidate until there is more than one missing
hit or the extrapolation does not find another tracker layer.
3. Track fitting: A Kalman filter or a Gaussian-sum filter/smoother is performed to
obtain the final estimate of the track parameters at the interaction point exploiting
the full trajectory information.
4. Track selection: Tracks are grouped in classes according to different track
quality criteria.
As an example, the twelve tracking iterations foreseen for 2017 data taking is listed
in Table 13.3 [157]. The main difference between iterations is the configuration of
the seed generation and the target tracks.
Figure 13.7 shows the tracking efficiency, using a standard sample of tt events
simulated with
√
s = 13 TeV with different superimposed pileup conditions. The
contribution of different iterations for 2017 track reconstruction is also shown as a
function of the p T of the simulated particle. It can be seen how iterations targeting
low-p T tracks are more efficient in the region between 100 and 500 MeV.
601
of about 230 μm and allowing the reconstruction of the hit position in 3-D. In the
outer layers the sensors are single-sided, and the z resolution can be approximated
by the strip length over
√
12, or about 15 mm. In order to maintain excellent tracking
performance until the Long Shutdown 3 of LHC, the Pixel Tracker was replaced in
the year-end technical stop of 2016/2017 with a new Pixel Tracker composed of four
barrel layers and six forward disks providing four-hit pixel coverage up to |η| = 2.5.
After the Long Shutdown 3, the High Luminosity phase of the LHC (HL-LHC)
is scheduled where the accelerator will provide an unprecedented instantaneous
luminosity of 5 − 7.5 × 10 34 cm −2 s −1 . In [155] the new CMS silicon tracker and its
tracking and vertexing performance for different event types, pileup scenarios and
detector geometries are presented.
The CMS track reconstruction algorithm is based on an iterative approach [156].
The main idea is to search for easier-to-find tracks first, to mask the hits associated to
the found tracks, and to proceed to the next iteration. In this way the combinatorial
problem is reduced, and the search for more difficult classes of tracks is simplified.
Moreover, this approach introduces the possibility of developing special iterations
that can improve track reconstruction in high-density environments such as jets,
or to use the information from other subsystems such as muon chambers and
calorimeters. In each iteration, the Combinatorial Track Finder is run. It can be
divided into four different steps:
1. Seed generation: Using the information of three or four hits, the trajectory
parameters and the corresponding uncertainties of the initial track candidates are
computed.
2. Track finding: Staring from the seed, the current trajectory parameters and
their uncertainties are extrapolated to the next layer and compatible hits are
found. Each of them is added to a clone of the track candidate. Each of these
candidates is again extrapolated to the next layer and compatible hits are found.
This procedure is repeated for each candidate until there is more than one missing
hit or the extrapolation does not find another tracker layer.
3. Track fitting: A Kalman filter or a Gaussian-sum filter/smoother is performed to
obtain the final estimate of the track parameters at the interaction point exploiting
the full trajectory information.
4. Track selection: Tracks are grouped in classes according to different track
quality criteria.
As an example, the twelve tracking iterations foreseen for 2017 data taking is listed
in Table 13.3 [157]. The main difference between iterations is the configuration of
the seed generation and the target tracks.
Figure 13.7 shows the tracking efficiency, using a standard sample of tt events
simulated with
√
s = 13 TeV with different superimposed pileup conditions. The
contribution of different iterations for 2017 track reconstruction is also shown as a
function of the p T of the simulated particle. It can be seen how iterations targeting
low-p T tracks are more efficient in the region between 100 and 500 MeV.