The cuts used to define selected tracks were introduced in Section 3. These cuts were designed to select only well-reconstructed primary tracks for which the tracking efficiency could be accurately estimated. The cuts on the impact parameters,|dP V0 |<1.5mm and|z0P V sinθ|<1.5 mm reject a significant fraction of tracks from secondary particles. The largest discriminatory power is provided by using impact parameters expressed at the primary vertex, therefore the track reconstruction efficiency was only measured for events containing a primary vertex. The cuts on the number of pixel and SCT hits were optimised to minimise inefficiencies due to inactive detector elements while controlling the rate of fake and mismeasured tracks. No requirement was placed on the number of TRT hits. Section 6.3 compares the properties of the distributions used for track selection between data and simulation.
Figure 8.5 shows the fraction of reconstructed tracks after each track selection cut is applied sequentially as a function of trackη. The efficiency of the pixel and SCT hit cuts is well-described by the simulation, however the efficiency of the impact parameter cuts are a few % higher in simulation than data. This does not necessarily mean that the primary track reconstruction efficiency differs between data and simulation, because the cumulative efficiency is calculated with respect to all reconstructed tracks.
η
-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5
Cumulative Cut Efficiency
0.75 0.8 0.85 0.9 0.95 1
[ Data, MC ] >=1 Pixel Hit [ Data, MC ] >=6 SCT Hits [ Data, | < 1.5 mm
MC ] |d0
[ Data, sin θ| < 1.5mm MC ] |z0
ATLAS Preliminary
= 900 GeV s
Figure 8.5: The cumulative efficiency of each track selection cut with respect to all reconstructed tracks in data and simulation as a function ofη at √
s= 900 GeV. The efficiency in data is the solid histogram and the efficiency in simulation is the dashed histogram.
The systematic uncertainties due to the track selection cuts were assessed using the N −1 cut technique. This compares the efficiency of each track selection cut in data and simulation by calculating the ratio of the number of tracks after all cuts to the number of tracks with all cuts but the cut in question, i.e.:
N−1cut = NtrkN cuts
NtrkN−1 cuts (8.5)
For example, the N −1 efficiency of the pixel hit cut is number of tracks passing the d0, z0, pixel and SCT hit cuts to the number of tracks passing thed0, z0 and SCT hit cut. As no truth information is used, the efficiency can be measured in data as well as in simulation.
Figure 8.6 compares theN−1efficiency obtained in data and simulation. TheN−1efficiency is shown for the cut used to define the selected tracks, from which the systematic uncertainty was estimated, and for a tighter cut, which accentuates any possible differences between data and simulation. Because the tails of thed0 distribution were used to estimate the fraction secondaries, a slightly different definition was used to avoid double counting it as a systematic uncertainty.
Instead of removing thed0 cut completely to define theN−1efficiency, the cut value was varied by±0.5mm.
Figure 8.6(a) shows that requiring a single hit in the pixel detector rejects very few tracks, whereas a requirement of two pixel hits would result in an efficiency varying as a function of η.
This variation is the result of the location of inactive pixel modules. For this reason and because theN−1 efficiency for two pixel hits is not fully described by the simulation, only a single pixel hit was required.
TheN−1efficiency of requiring six hits in the SCT varies strongly as a function ofη (Fig. 8.6(b)) due to the varying number of SCT layers that a particle passes through. The shape is well described by the simulation except for small differences at large value of the pseudorapidity.
For the impact parameter cuts, theN−1efficiency decreases with increasingη as the impact parameter resolution worsens. Small differences in the efficiency between data and simulation are visible at the edge of the detector acceptance. The efficiency is not symmetric inηbecause collisions occurred more often at negative than positivez.
The difference in theN−1efficiency of each cut between data and simulation is shown in Fig. 8.7.
This difference was used for the systematic uncertainty due to the Monte Carlo modelling of each selection cut. As the correlations between the different cuts were not studied, the uncertainties
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>= 5 SCT Hits (Data
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cut0all but loose d/Nall cutsN
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| < 2 (Data
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(c) Transverse Impact Parameter
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=900 GeV) s )| < 1.5 (Data sin(θ
|z0
=900 GeV) s )| < 1.5 (Non-diffractive MC MinBias sin(θ
|z0
=900 GeV) s ) | < 1 (Data θ sin(
|z0
=900 GeV) s ) | < 1 (Non-diffractive MC MinBias θ
sin(
|z0
(d) Longitudinal Impact Parameter
Figure 8.6: The N −1 efficiency of the four track selection cuts in data (solid histogram) and simulation (dashed histogram) at√
s= 900GeV.
were conservatively assumed to be fully correlated. Therefore the total systematic uncertainty due to selection cuts shown in Fig. 8.7(b) was calculated from the linear sum of the absolute value of the difference for each cut. Generally, the total systematic uncertainty is less than 1%.
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Systematic Uncertainty
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>= 1 Pixel Hit
>= 6 SCT Hits
| 1.5 - 2 mm
|d0
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)| < 1.5 mm sin(θ
|z0
(a) Individual Cuts
η -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5
Selection Systematic Uncertainty
0 0.005 0.01 0.015 0.02 0.025
(b) Total
Figure 8.7: The difference between theN −1 efficiency for each track selection cut in data and simulation (a) and the total estimated systematic uncertainty due to the selection cuts from the linear of the individual uncertainties (b) at√
s= 900 GeV