CMSMUO17001 ; CERNEP2019238  
Performance of the reconstruction and identification of highmomentum muons in protonproton collisions at $\sqrt{s} = $ 13 TeV  
CMS Collaboration  
7 December 2019  
JINST 15 (2020) P02027  
Abstract: The CMS detector at the LHC has recorded events from protonproton collisions, with muon momenta reaching up to 1.8 TeV in the collected dimuon samples. These highmomentum muons allow direct access to new regimes in physics beyond the standard model. Because the physics and reconstruction of these muons are different from those of their lowermomentum counterparts, this paper presents for the first time dedicated studies of efficiencies, momentum assignment, resolution, scale, and showering of very high momentum muons produced at the LHC. These studies are performed using the 2016 and 2017 data sets of protonproton collisions at $\sqrt{s} = $ 13 TeV with integrated luminosities of 36.3 and 42.1 fb$^{1}$, respectively.  
Links: eprint arXiv:1912.03516 [hepex] (PDF) ; CDS record ; inSPIRE record ; CADI line (restricted) ; 
Figures  
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Figure 1:
Fraction of choices of different refit algorithms chosen by TuneP, comparing 2016+2017 data and DY simulation for five $ {p_{\mathrm {T}}} $ ranges and for two $\eta $ categories: (left) barrel with $ { \eta } < $ 1.2 and (right) endcap with 1.2 $ < { \eta } < $ 2.4. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 1a:
Fraction of choices of different refit algorithms chosen by TuneP, comparing 2016+2017 data and DY simulation for five $ {p_{\mathrm {T}}} $ ranges in the $\eta $ category: barrel with $ { \eta } < $ 1.2. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 1b:
Fraction of choices of different refit algorithms chosen by TuneP, comparing 2016+2017 data and DY simulation for five $ {p_{\mathrm {T}}} $ ranges in the $\eta $ category: endcap with 1.2 $ < { \eta } < $ 2.4. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 2:
The probability $ {P_{\text {shower}}} (p)$ to tag at least one shower in any of the four stations, as a function of the incoming muon momentum, for (upper left) DTs; (upper right) CSCs with muon $ { \eta } < $ 1.8; and (lower) CSCs with muon $ { \eta } > $ 1.8. Results are evaluated for the shower tagging definition requiring $ {N_{\text {seg}}} \ge $ 2. Different colors refer to: data (black), DY simulation (red), and single muons simulated with a uniform $p$ distribution (blue). 
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Figure 2a:
The probability $ {P_{\text {shower}}} (p)$ to tag at least one shower in any of the four stations, as a function of the incoming muon momentum, for DTs. Results are evaluated for the shower tagging definition requiring $ {N_{\text {seg}}} \ge $ 2. Different colors refer to: data (black), DY simulation (red), and single muons simulated with a uniform $p$ distribution (blue). 
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Figure 2b:
The probability $ {P_{\text {shower}}} (p)$ to tag at least one shower in any of the four stations, as a function of the incoming muon momentum, for CSCs with muon $ { \eta } < $ 1.8. Results are evaluated for the shower tagging definition requiring $ {N_{\text {seg}}} \ge $ 2. Different colors refer to: data (black), DY simulation (red), and single muons simulated with a uniform $p$ distribution (blue). 
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Figure 2c:
The probability $ {P_{\text {shower}}} (p)$ to tag at least one shower in any of the four stations, as a function of the incoming muon momentum, for CSCs with muon $ { \eta } > $ 1.8. Results are evaluated for the shower tagging definition requiring $ {N_{\text {seg}}} \ge $ 2. Different colors refer to: data (black), DY simulation (red), and single muons simulated with a uniform $p$ distribution (blue). 
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Figure 3:
Comparison between the efficiency of Run 2 and Run 1 high$ {p_{\mathrm {T}}}$ ID, as a function of (left) $\eta $ and (right) ${p_{\mathrm {T}}}$. The efficiencies are obtained from dimuon events with a mass greater than 120 GeV to further select the highmass DY process. The top panel shows the data to simulation efficiency ratio obtained for the Run 1 (blue squares) and for the Run 2 high$ {p_{\mathrm {T}}}$ ID (black circles). The bottom panel shows the Run 2 to Run 1 high$ {p_{\mathrm {T}}}$ ID efficiency ratio obtained from the data (black circles) and from simulation (red triangles). The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 3a:
Comparison between the efficiency of Run 2 and Run 1 high$ {p_{\mathrm {T}}}$ ID, as a function of $\eta $. The efficiencies are obtained from dimuon events with a mass greater than 120 GeV to further select the highmass DY process. The top panel shows the data to simulation efficiency ratio obtained for the Run 1 (blue squares) and for the Run 2 high$ {p_{\mathrm {T}}}$ ID (black circles). The bottom panel shows the Run 2 to Run 1 high$ {p_{\mathrm {T}}}$ ID efficiency ratio obtained from the data (black circles) and from simulation (red triangles). The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 3b:
Comparison between the efficiency of Run 2 and Run 1 high$ {p_{\mathrm {T}}}$ ID, as a function of ${p_{\mathrm {T}}}$. The efficiencies are obtained from dimuon events with a mass greater than 120 GeV to further select the highmass DY process. The top panel shows the data to simulation efficiency ratio obtained for the Run 1 (blue squares) and for the Run 2 high$ {p_{\mathrm {T}}}$ ID (black circles). The bottom panel shows the Run 2 to Run 1 high$ {p_{\mathrm {T}}}$ ID efficiency ratio obtained from the data (black circles) and from simulation (red triangles). The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 4:
High$ {p_{\mathrm {T}}}$ ID efficiency for 2016 and 2017 data, and corresponding DY simulation, as a function of ${p_{\mathrm {T}}}$ for (upper left) $ { \eta } < $ 0.9, (upper right) 0.9 $ < { \eta } < $ 1.2, (lower left) 1.2 $ < { \eta } < $ 2.1, and (lower right) 2.1 $ < { \eta } < $ 2.4. The black circles represent data; the red triangles represent DY simulation. The datatosimulation ratio, also called the datatosimulation scale factor (SF), is displayed in the lower panels. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 4a:
High$ {p_{\mathrm {T}}}$ ID efficiency for 2016 and 2017 data, and corresponding DY simulation, as a function of ${p_{\mathrm {T}}}$ for $ { \eta } < $ 0.9. The black circles represent data; the red triangles represent DY simulation. The datatosimulation ratio, also called the datatosimulation scale factor (SF), is displayed in the lower panels. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 4b:
High$ {p_{\mathrm {T}}}$ ID efficiency for 2016 and 2017 data, and corresponding DY simulation, as a function of ${p_{\mathrm {T}}}$ for 0.9 $ < { \eta } < $ 1.2. The black circles represent data; the red triangles represent DY simulation. The datatosimulation ratio, also called the datatosimulation scale factor (SF), is displayed in the lower panels. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 4c:
High$ {p_{\mathrm {T}}}$ ID efficiency for 2016 and 2017 data, and corresponding DY simulation, as a function of ${p_{\mathrm {T}}}$ for 1.2 $ < { \eta } < $ 2.1. The black circles represent data; the red triangles represent DY simulation. The datatosimulation ratio, also called the datatosimulation scale factor (SF), is displayed in the lower panels. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 4d:
High$ {p_{\mathrm {T}}}$ ID efficiency for 2016 and 2017 data, and corresponding DY simulation, as a function of ${p_{\mathrm {T}}}$ for 2.1 $ < { \eta } < $ 2.4. The black circles represent data; the red triangles represent DY simulation. The datatosimulation ratio, also called the datatosimulation scale factor (SF), is displayed in the lower panels. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 5:
The $N1$ efficiencies, for $ {p_{\mathrm {T}}} > $ 53 GeV and binned in $\eta $, comparison between 2016 and 2017 data sets and for the corresponding DY simulations, for (upper left) $ { \eta } < $ 0.9, (upper right) 0.9 $ < { \eta } < $ 1.2, (lower left) 1.2 $ < { \eta } < $ 2.1, and (lower right) 2.1 $ < { \eta } < $ 2.4. The black circles represent 2016 data; the blue squares represent 2017 data. The lower panels display the ratio of $N1$ efficiencies obtained for each of the criteria, between 2017 and 2016 data sets, and between data and their corresponding simulations for both years. 
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Figure 5a:
The $N1$ efficiencies, for $ {p_{\mathrm {T}}} > $ 53 GeV and binned in $\eta $, comparison between 2016 and 2017 data sets and for the corresponding DY simulations, for $ { \eta } < $ 0.9. The black circles represent 2016 data; the blue squares represent 2017 data. The lower panels display the ratio of $N1$ efficiencies obtained for each of the criteria, between 2017 and 2016 data sets, and between data and their corresponding simulations for both years. 
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Figure 5b:
The $N1$ efficiencies, for $ {p_{\mathrm {T}}} > $ 53 GeV and binned in $\eta $, comparison between 2016 and 2017 data sets and for the corresponding DY simulations, for 0.9 $ < { \eta } < $ 1.2. The black circles represent 2016 data; the blue squares represent 2017 data. The lower panels display the ratio of $N1$ efficiencies obtained for each of the criteria, between 2017 and 2016 data sets, and between data and their corresponding simulations for both years. 
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Figure 5c:
The $N1$ efficiencies, for $ {p_{\mathrm {T}}} > $ 53 GeV and binned in $\eta $, comparison between 2016 and 2017 data sets and for the corresponding DY simulations, for 1.2 $ < { \eta } < $ 2.1. The black circles represent 2016 data; the blue squares represent 2017 data. The lower panels display the ratio of $N1$ efficiencies obtained for each of the criteria, between 2017 and 2016 data sets, and between data and their corresponding simulations for both years. 
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Figure 5d:
The $N1$ efficiencies, for $ {p_{\mathrm {T}}} > $ 53 GeV and binned in $\eta $, comparison between 2016 and 2017 data sets and for the corresponding DY simulations, for 2.1 $ < { \eta } < $ 2.4. The black circles represent 2016 data; the blue squares represent 2017 data. The lower panels display the ratio of $N1$ efficiencies obtained for each of the criteria, between 2017 and 2016 data sets, and between data and their corresponding simulations for both years. 
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Figure 6:
High$ {p_{\mathrm {T}}}$ ID efficiency for 2016+2017 data, and corresponding DY simulation, as a function of $p$ for (upper left) $ { \eta } < $ 0.9, (upper right) 1.2 $ < { \eta } < $ 2.1, and (lower) 2.1 $ < { \eta } < $ 2.4. The blue squares show efficiency for muons in data with no showers tagged; the green inverted triangles show the same for muons in DY simulation. The black circles correspond to muons in data with at least one shower tag, while the red triangles are the same for muons in DY simulation. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 6a:
High$ {p_{\mathrm {T}}}$ ID efficiency for 2016+2017 data, and corresponding DY simulation, as a function of $p$ for $ { \eta } < $ 0.9. The blue squares show efficiency for muons in data with no showers tagged; the green inverted triangles show the same for muons in DY simulation. The black circles correspond to muons in data with at least one shower tag, while the red triangles are the same for muons in DY simulation. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 6b:
High$ {p_{\mathrm {T}}}$ ID efficiency for 2016+2017 data, and corresponding DY simulation, as a function of $p$ for 1.2 $ < { \eta } < $ 2.1. The blue squares show efficiency for muons in data with no showers tagged; the green inverted triangles show the same for muons in DY simulation. The black circles correspond to muons in data with at least one shower tag, while the red triangles are the same for muons in DY simulation. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 6c:
High$ {p_{\mathrm {T}}}$ ID efficiency for 2016+2017 data, and corresponding DY simulation, as a function of $p$ for 2.1 $ < { \eta } < $ 2.4. The blue squares show efficiency for muons in data with no showers tagged; the green inverted triangles show the same for muons in DY simulation. The black circles correspond to muons in data with at least one shower tag, while the red triangles are the same for muons in DY simulation. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 7:
Standalone muon reconstruction efficiency as a function of muon $\eta $ for the (left) 2016 and (right) 2017 data sets. The blue points represent the data, while the red empty squares represent the simulation. 
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Figure 7a:
Standalone muon reconstruction efficiency as a function of muon $\eta $ for the 2016 data set. The blue points represent the data, while the red empty squares represent the simulation. 
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Figure 7b:
Standalone muon reconstruction efficiency as a function of muon $\eta $ for the 2017 data set. The blue points represent the data, while the red empty squares represent the simulation. 
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Figure 8:
Standalone muon reconstruction efficiency as a function of muon momentum in two different $ { \eta }$ regions: (left) $ { \eta } < $ 1.6, and (right) forward endcaps from, 1.6 $ < { \eta } < $ 2.4. The upper row shows the 2016 results, with blue points representing data and red empty squares representing simulation. The lower row shows the 2017 results. The lower panels of the plots show the ratio of data to simulation. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 8a:
Standalone muon reconstruction efficiency as a function of muon momentum in the $ { \eta }$ region $ { \eta } < $ 1.6. The upper row shows the 2016 results, with blue points representing data and red empty squares representing simulation. The lower row shows the 2017 results. The lower panel of the plot shows the ratio of data to simulation. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 8b:
Standalone muon reconstruction efficiency as a function of muon momentum in the $ { \eta }$ region 1.6 $ < { \eta } < $ 2.4. The upper row shows the 2016 results, with blue points representing data and red empty squares representing simulation. The lower row shows the 2017 results. The lower panel of the plot shows the ratio of data to simulation. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 8c:
Standalone muon reconstruction efficiency as a function of muon momentum in two different $ { \eta }$ regions: (left) $ { \eta } < $ 1.6, and (right) forward endcaps from, 1.6 $ < { \eta } < $ 2.4. The upper row shows the 2016 results, with blue points representing data and red empty squares representing simulation. The lower row shows the 2017 results. The lower panels of the plots show the ratio of data to simulation. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 8d:
Standalone muon reconstruction efficiency as a function of muon momentum in two different $ { \eta }$ regions: (left) $ { \eta } < $ 1.6, and (right) forward endcaps from, 1.6 $ < { \eta } < $ 2.4. The upper row shows the 2016 results, with blue points representing data and red empty squares representing simulation. The lower row shows the 2017 results. The lower panels of the plots show the ratio of data to simulation. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 9:
Standalone muon reconstruction efficiency as a function of muon $p$ for muons with 1.6 $ < { \eta } < $ 2.4. The left plots are for low pileup (up to 15 vertices) while the right plots are for higher pileup. The upper plots are obtained with events without any showers; the lower ones contain events with at least one shower. The blue points represent data and the red empty squares represent simulation. The lower panels of the plots show the ratio of data to simulation. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 9a:
Standalone muon reconstruction efficiency as a function of muon $p$ for muons with 1.6 $ < { \eta } < $ 2.4 for low pileup (up to 15 vertices). The plot is obtained with events without any showers. The blue points represent data and the red empty squares represent simulation. The lower panel of the plot shows the ratio of data to simulation. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 9b:
Standalone muon reconstruction efficiency as a function of muon $p$ for muons with 1.6 $ < { \eta } < $ 2.4 for high pileup (higher than 15 vertices). The plot is obtained with events without any showers. The blue points represent data and the red empty squares represent simulation. The lower panel of the plot shows the ratio of data to simulation. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 9c:
Standalone muon reconstruction efficiency as a function of muon $p$ for muons with 1.6 $ < { \eta } < $ 2.4 for low pileup (up to 15 vertices). The plot contains events with at least one shower. The blue points represent data and the red empty squares represent simulation. The lower panel of the plot shows the ratio of data to simulation. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 9d:
Standalone muon reconstruction efficiency as a function of muon $p$ for muons with 1.6 $ < { \eta } < $ 2.4 for high pileup (higher than 15 vertices). The plot contains events with at least one shower. The blue points represent data and the red empty squares represent simulation. The lower panel of the plot shows the ratio of data to simulation. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 10:
Global muon reconstruction efficiency as a function of muon momentum. The left plot is obtained with events without any showers, while the right one contains events with at least one shower. The blue points represent data and the red empty squares represent simulation. The lower panels of the plots show the ratio of data to simulation. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 10a:
Global muon reconstruction efficiency as a function of muon momentum. The plot is obtained with events without any showers. The blue points represent data and the red empty squares represent simulation. The lower panel shows the ratio of data to simulation. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 10b:
Global muon reconstruction efficiency as a function of muon momentum. The plot contains events with at least one shower. The blue points represent data and the red empty squares represent simulation. The lower panel shows the ratio of data to simulation. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 11:
The combined HLT+L1 efficiency with respect to the offline selection, and the ratio of data to simulation for different methods, as functions of ${p_{\mathrm {T}}}$, for (left) 2016 data and (right) 2017 data. The red triangles are measured using an independent data set collected with a ${{p_{\mathrm {T}}} ^\text {miss}}$ trigger; the black circles are measured by the extended tagandprobe method in which selected events have $m_{\mu \mu} > $ 120 GeV. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 11a:
The combined HLT+L1 efficiency with respect to the offline selection, and the ratio of data to simulation for different methods, as functions of ${p_{\mathrm {T}}}$, for (left) 2016 data and (right) 2017 data. The red triangles are measured using an independent data set collected with a ${{p_{\mathrm {T}}} ^\text {miss}}$ trigger; the black circles are measured by the extended tagandprobe method in which selected events have $m_{\mu \mu} > $ 120 GeV. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 11b:
The combined HLT+L1 efficiency with respect to the offline selection, and the ratio of data to simulation for different methods, as functions of ${p_{\mathrm {T}}}$, for (left) 2016 data and (right) 2017 data. The red triangles are measured using an independent data set collected with a ${{p_{\mathrm {T}}} ^\text {miss}}$ trigger; the black circles are measured by the extended tagandprobe method in which selected events have $m_{\mu \mu} > $ 120 GeV. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 12:
The combined HLT+L1 efficiency with respect to the offline selection, and the ratio of data to simulation, as a function of ${p_{\mathrm {T}}}$, for (upper) 2016 data and (lower) 2017 data and simulation. The left plots are for the barrel region and the right plots are for the overlap region. The red triangles represent the simulation while the black dots are the data. The lower panels display the ratio of efficiencies in data and simulation. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 12a:
The combined HLT+L1 efficiency with respect to the offline selection, and the ratio of data to simulation, as a function of ${p_{\mathrm {T}}}$, for 2016 data and simulation. The plot is for the barrel region. The red triangles represent the simulation while the black dots are the data. The lower panel displays the ratio of efficiencies in data and simulation. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 12b:
The combined HLT+L1 efficiency with respect to the offline selection, and the ratio of data to simulation, as a function of ${p_{\mathrm {T}}}$, for 2016 data and simulation. The plot is for the overlap region. The red triangles represent the simulation while the black dots are the data. The lower panel displays the ratio of efficiencies in data and simulation. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 12c:
The combined HLT+L1 efficiency with respect to the offline selection, and the ratio of data to simulation, as a function of ${p_{\mathrm {T}}}$, for 2017 data and simulation. The plot is for the barrel region. The red triangles represent the simulation while the black dots are the data. The lower panel displays the ratio of efficiencies in data and simulation. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 12d:
The combined HLT+L1 efficiency with respect to the offline selection, and the ratio of data to simulation, as a function of ${p_{\mathrm {T}}}$, for 2017 data and simulation. The plot is for the overlap region. The red triangles represent the simulation while the black dots are the data. The lower panel displays the ratio of efficiencies in data and simulation. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 13:
The L1 efficiency in three $\eta $ regions: (upper left) barrel; (upper right) for muon with 1.2 $ < { \eta } < $ 1.8; and (lower) endcap with muon $ { \eta } > $ 1.8. The plots show a comparison between directly determining the efficiency from simulation (blue dots) and with data (black triangles) with respect to calculating it from shower multiplicity, both in 2016+2017 combined data (black line) and 2017 simulation (dashed blue line). The shaded bands include the statistical uncertainties of the measurements and the systematic uncertainty of the showering probability determination. 
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Figure 13a:
The L1 efficiency in the barrel region. The plot shows a comparison between directly determining the efficiency from simulation (blue dots) and with data (black triangles) with respect to calculating it from shower multiplicity, both in 2016+2017 combined data (black line) and 2017 simulation (dashed blue line). The shaded bands include the statistical uncertainties of the measurements and the systematic uncertainty of the showering probability determination. 
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Figure 13b:
The L1 efficiency for muon with 1.2 $ < { \eta } < $ 1.8. The plot shows a comparison between directly determining the efficiency from simulation (blue dots) and with data (black triangles) with respect to calculating it from shower multiplicity, both in 2016+2017 combined data (black line) and 2017 simulation (dashed blue line). The shaded bands include the statistical uncertainties of the measurements and the systematic uncertainty of the showering probability determination. 
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Figure 13c:
The L1 efficiency in the endcap with muon $ { \eta } > $ 1.8. The plot shows a comparison between directly determining the efficiency from simulation (blue dots) and with data (black triangles) with respect to calculating it from shower multiplicity, both in 2016+2017 combined data (black line) and 2017 simulation (dashed blue line). The shaded bands include the statistical uncertainties of the measurements and the systematic uncertainty of the showering probability determination. 
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Figure 14:
Muon momentum resolution (standard deviation $\sigma $ of the fit of the core distributions to a Gaussian function) in (left) the barrel region $ { \eta } < $ 0.9 and (right) the endcap region 1.2 $ < { \eta } < $ 2.4, for the TuneP algorithm, as a function of muon momentum, for the various misalignment scenarios with and without APEs. A comparison with the ideal scenario is also given. The performance of the trackeronly fit is shown for comparison. 
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Figure 14a:
Muon momentum resolution (standard deviation $\sigma $ of the fit of the core distributions to a Gaussian function) in the barrel region $ { \eta } < $ 0.9, for the TuneP algorithm, as a function of muon momentum, for the various misalignment scenarios with and without APEs. A comparison with the ideal scenario is also given. The performance of the trackeronly fit is shown for comparison. 
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Figure 14b:
Muon momentum resolution (standard deviation $\sigma $ of the fit of the core distributions to a Gaussian function) for the TuneP algorithm, as a function of muon momentum, for the various misalignment scenarios with and without APEs. A comparison with the ideal scenario is also given. The performance of the trackeronly fit is shown for comparison. 
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Figure 15:
Comparison of the TuneP and global reconstruction algorithms for simulated muons in the (upper) barrel and (lower) endcap, for the cases with and without the presence of tagged showers in any muon station. The left plots show the momentum resolution (Gaussian $\sigma $); the right plots show the tail fraction with $ { \delta k /k } > $ 20%, as a function of muon momentum. 
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Figure 15a:
Comparison of the TuneP and global reconstruction algorithms for simulated muons in the barrel, for the cases with and without the presence of tagged showers in any muon station. The plot shows the momentum resolution (Gaussian $\sigma $), as a function of muon momentum. 
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Figure 15b:
Comparison of the TuneP and global reconstruction algorithms for simulated muons in the barrel, for the cases with and without the presence of tagged showers in any muon station. The plot shows the momentum resolution (Gaussian $\sigma $), as a function of muon momentum. 
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Figure 15c:
Comparison of the TuneP and global reconstruction algorithms for simulated muons in the endcap, for the cases with and without the presence of tagged showers in any muon station. The plot shows the tail fraction with $ { \delta k /k } > $ 20%, as a function of muon momentum. 
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Figure 15d:
Comparison of the TuneP and global reconstruction algorithms for simulated muons in the endcap, for the cases with and without the presence of tagged showers in any muon station. The plot shows the tail fraction with $ { \delta k /k } > $ 20%, as a function of muon momentum. 
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Figure 16:
Gaussian $\sigma $ of fits to $q/ {p_{\mathrm {T}}} $ relative residuals for TuneP cosmic ray muons collected in 2016 and 2017 for (left) the barrel ($ { \eta } < $ 1.2) and (right) the endcap (1.2 $ < { \eta } < $ 1.6) regions, compared to the resolution extracted from DY simulation. 
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Figure 16a:
Gaussian $\sigma $ of fits to $q/ {p_{\mathrm {T}}} $ relative residuals for TuneP cosmic ray muons collected in 2016 and 2017 for the barrel ($ { \eta } < $ 1.2) region, compared to the resolution extracted from DY simulation. 
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Figure 16b:
Gaussian $\sigma $ of fits to $q/ {p_{\mathrm {T}}} $ relative residuals for TuneP cosmic ray muons collected in 2016 and 2017 for the endcap (1.2 $ < { \eta } < $ 1.6) region, compared to the resolution extracted from DY simulation. 
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Figure 17:
Dimuon mass resolution (Gaussian $\sigma $), as a function of muon TuneP ${p_{\mathrm {T}}}$ in the BB category. Each dimuon event is counted twice since each muon ($\mu ^+$ and $\mu ^$) in the event is filling the histograms. Results for (left) 2016 and (right) 2017 are shown. Data are shown in black while the resolution obtained from simulation is shown in red. The lower panels of the plots show the ratio of data to simulation; the blue boxes represent the statistical uncertainties. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 17a:
Dimuon mass resolution (Gaussian $\sigma $), as a function of muon TuneP ${p_{\mathrm {T}}}$ in the BB category. Each dimuon event is counted twice since each muon ($\mu ^+$ and $\mu ^$) in the event is filling the histograms. Results for 2016 are shown. Data are shown in black while the resolution obtained from simulation is shown in red. The lower panel shows the ratio of data to simulation; the blue boxes represent the statistical uncertainties. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 17b:
Dimuon mass resolution (Gaussian $\sigma $), as a function of muon TuneP ${p_{\mathrm {T}}}$ in the BB category. Each dimuon event is counted twice since each muon ($\mu ^+$ and $\mu ^$) in the event is filling the histograms. Results for 2017 are shown. Data are shown in black while the resolution obtained from simulation is shown in red. The lower panel shows the ratio of data to simulation; the blue boxes represent the statistical uncertainties. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 18:
Dimuon mass resolution (Gaussian $\sigma $), as a function of muon TuneP ${p_{\mathrm {T}}}$ in the BE+EE category. Each dimuon event is counted twice since each muon ($\mu ^+$ and $\mu ^$) in the event is filling the histograms. Results for (upper left) 2016 and (upper right) 2017 are shown. The lower plot is for 2017 data where the BE+EE category is defined with at least one of the two muons with $ { \eta } > $ 1.6. Data are shown in black while the resolution obtained from simulation is shown in red. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 18a:
Dimuon mass resolution (Gaussian $\sigma $), as a function of muon TuneP ${p_{\mathrm {T}}}$ in the BE+EE category. Each dimuon event is counted twice since each muon ($\mu ^+$ and $\mu ^$) in the event is filling the histograms. Results for 2016 are shown. Data are shown in black while the resolution obtained from simulation is shown in red. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 18b:
Dimuon mass resolution (Gaussian $\sigma $), as a function of muon TuneP ${p_{\mathrm {T}}}$ in the BE+EE category. Each dimuon event is counted twice since each muon ($\mu ^+$ and $\mu ^$) in the event is filling the histograms. Results for 2017 are shown. Data are shown in black while the resolution obtained from simulation is shown in red. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 18c:
Dimuon mass resolution (Gaussian $\sigma $), as a function of muon TuneP ${p_{\mathrm {T}}}$ in the BE+EE category. Each dimuon event is counted twice since each muon ($\mu ^+$ and $\mu ^$) in the event is filling the histograms. The plot is for 2017 data where the BE+EE category is defined with at least one of the two muons with $ { \eta } > $ 1.6. Data are shown in black while the resolution obtained from simulation is shown in red. The central value in each bin is obtained from the average of the distribution within the bin. 
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Figure 19:
Data to simulation comparison of the curvature distribution in $\mathrm{Z} \to \mu ^+\mu ^$ events, for 2017 data with muon $ {p_{\mathrm {T}}} > $ 200 GeV. 
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Figure 20:
Measurement of the scale bias for muons above 200 GeV with 2016 data. On the left the ${p_{\mathrm {T}}}$ corresponds to TuneP, while on the right it corresponds to the trackeronly assignment. 
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Figure 20a:
Measurement of the scale bias for muons above 200 GeV with 2016 data. The ${p_{\mathrm {T}}}$ corresponds to TuneP. 
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Figure 20b:
Measurement of the scale bias for muons above 200 GeV with 2016 data. The ${p_{\mathrm {T}}}$ corresponds to the trackeronly assignment. 
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Figure 21:
Measurement of the scale bias for muons above 200 GeV with 2017 data. On the left the ${p_{\mathrm {T}}}$ corresponds to the TuneP, while on the right it corresponds to the trackeronly assignment. 
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Figure 21a:
Measurement of the scale bias for muons above 200 GeV with 2017 data. The ${p_{\mathrm {T}}}$ corresponds to the TuneP. 
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Figure 21b:
Measurement of the scale bias for muons above 200 GeV with 2017 data. The ${p_{\mathrm {T}}}$ corresponds to the trackeronly assignment. 
Tables  
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Table 1:
The L1 trigger efficiency for barrel and endcap muons measured as a function of the number of showers in the muon stations. The endcap was split into near ($ { \eta } < $ 1.8) and far ($ { \eta } > $ 1.8) sections. 
Summary 
The performance of muon reconstruction, identification, trigger, and momentum assignment has been studied in a sample enriched in highmomentum muons using protonproton collisions at $\sqrt{s} = $ 13 TeV, collected by the CMS experiment at the LHC in 20162017, and corresponding to the integrated luminosity of 78.4 fb$^{1}$. Depending on the longitudinal component of the momentum, muons with transverse momentum ${p_{\mathrm{T}}} > $ 200 GeV can have radiative energy losses in steel that are no longer negligible compared to ionization energy losses. Dedicated methods have been developed to study the performance impact of the detector alignment and electromagnetic showers along the muon track. Overall, the measurements are described accurately by the simulation and their reach in momentum is limited by the statistical uncertainties. The largest discrepancy between data and simulation is found at the trigger level with a 10% efficiency difference for muons with ${p_{\mathrm{T}}}$ around 1 TeV. Representative figures of merit that illustrate the muon performance at high momentum are listed below. 
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