re-calculating the resultingc-jet tagging efficiency. The uncertainties due to jet energy scale and resolution determinations are estimated in the same way. The uncertainties on the lepton and jet energy scale and resolution are additionally propagated to the reconstruction of the missing trans- verse momentum. Further systematic uncertainties that affect theETmissreconstruction, but are not associated with reconstructed objects, are also accounted for. The systematic uncertainties due to the jet energy scale and resolution calibrations dominate the event reconstruction uncertainties, but are of the same order as the statistical uncertainty due to the limited size of the simulated signal sample. A more detailed breakdown and discussion of the event reconstruction uncertainties can be found in ref. [71].
Pre-tag yields and background tagging rates
The determination of the OS-SS background yields at pre-tag level and the assessment of the corre- sponding uncertainties are discussed in section12.3. The main source of systematic uncertainties is the data-driven estimation of the OS/SS asymmetry of theW+light and multijet backgrounds. The uncertainty due to the background tagging rates is dominated by the uncertainty on theW+light tag- ging rate mainly because of the limited size of the simulated sample used to derive it, as discussed in section12.4.
Fragmentation and decay modelling
Thec-quark fragmentation andc-hadron decay properties are corrected to improve the modelling of the Alpgen+PYTHIA signal sample as described in section12.5. Whenever results from inde- pendent measurements are used to correct the MC description, the uncertainties assigned to those results are propagated to the extrapolated scale factors. This is done for the fragmentation fractions and the semileptonic decay branching fractions of the prominent weakly decayingc-hadrons as well as for the hadronic n-prong decay branching fractions of the D0 meson. Where corrections are derived from MC simulations because no measurements are available, the corresponding systematic uncertainties are assessed by comparing predictions from different MC generators. Hence, the dif- ference between the PYTHIA and HERWIG simulations is used to estimate the uncertainty due to the fragmentation function ofcquarks. The systematic uncertainty due to a possible mismodelling of thep∗distribution of the soft muon is evaluated from the difference between the EvtGen and PYTHIA simulations. The largest difference between the EvtGen and either the PYTHIA or HER- WIG simulations is used to estimate the uncertainties due to the hadronicn-prong decay branching fractions of the D+andDs mesons as well as theΛ+c baryon. The largest effect on the final scale factors computed for inclusive c jets arises from the correction of then-prong decay branching fractions of hadronically decayingchadrons. Since only semileptonically decayingc hadrons are used in the data measurement, uncertainties on the properties of hadronically decayingchadrons propagate fully to the scale factors for inclusivecjets.
12.7 Results
The data-to-simulation c-jet tagging efficiency scale factors for several operating points of the MV1 tagging algorithm with respect to aW+csample simulated with Alpgen+PYTHIA-default are
2016 JINST 11 P04008
bε b-tagging operating point 85% 75% 70% 60%
c-jet tagging efficiency scale factor
0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 MV1 tagging algorithm Scale factor (stat) Scale factor (stat+syst) ATLAS = 7 TeV s -1 L dt = 4.6 fb
∫
Inclusive c-jet sample (W+c)
Figure 46. Data-to-simulation c-jet tagging efficiency scale factors for inclusive c jets derived for the MV1 tagging algorithm with respect to the Alpgen+PYTHIA-default sample.
shown in figure46for the different operating points. Being applicable to inclusive samples ofcjets, these scale factors are derived from the measured c-jet tagging efficiency scale factors for SMT
cjets (see section12.4) by a simulation-based extrapolation procedure. The results range between 0.75 and 0.92, decreasing with increasing tightness of the operating point, while the assigned total uncertainties increase from 5 % to 13 %. There are three main sources of uncertainties that are of the same order: the statistical uncertainty, the systematic uncertainty on the measured scale factors for SMTcjets and the systematic uncertainty due to the extrapolation procedure described in section12.5. The main contribution to the latter is the limited knowledge of the charged particle multiplicity ofc-hadron decays.
13 c-jet tagging efficiency calibration using theD?method
In this section thec-jet tagging efficiency is measured using a sample of jets containingD?+mesons, by comparing the yield ofD?+mesons before and after the tagging requirement. The measurement is based on theD?+→D0(→K−π+)π+decay mode, and the contamination withD?+mesons that result fromb-hadron decays is measured with a fit to theD0pseudo-proper time distribution.
13.1 Data and simulation samples
The data sample used in the D? measurement was collected using a logical OR of inclusive jet triggers. These triggers have been heavily prescaled to a constant bandwidth of about 0.5 Hz each and reach an efficiency of 99% for events having the leading jet with an offline pT higher than the
corresponding trigger thresholds by a factor ranging between 1.5 and 2. Events with at least one jet withpTabove a given threshold at the highest trigger level are selected, and using a combination of
the inclusive jet triggers, the data set covers the entire 20–200 GeV jetpTrange used in the analysis.
The analysis makes use of a Monte Carlo simulated sample of multijet events. The samples used are equivalent to those used in the muon-based b-jet tagging efficiency measurement (see section8) with the exception that each event in the sample used in theD?analysis is required to contain aD?+meson, in the decay modeD0(→K−π+)π+. Approximately one million events have been simulated per ˆp⊥bin.