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Muon efficiency vs pion rejection after applying IsMuon&IsMuonUnbiased&PT>=800 selection, reweighed so that background P & PT match signal, reweighed to give more weight to high-nPVs events (see plot), 2016 calibration data |
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Muon efficiency vs proton rejection after applying IsMuon&IsMuonUnbiased&PT>=800 selection, reweighed so that background P & PT match signal, reweighed to give more weight to high-nPVs events (see plot), 2016 calibration data |
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Muon efficiency vs pion rejection after applying IsMuon&IsMuonUnbiased&PT>=800 selection, no kinematic reweighting, 2016 calibration data |
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Momentum-pseudorapidity distributions for the calibration samples used for GAN training: pions (top-left), kaons (top-right), muons (bottom-left) and protons (bottom-right). The distributions are weighted using the s-weights. The following 2016 calibration samples were used: |
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Weighted real-data and generated distributions of RichDLLk for kaon and pion track candidates in bins of pseudorapidity (ETA) and momentum (P) over full phase-space. |
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Weighted real-data and generated distributions of RichDLLk for kaon and pion track candidates in bins of pseudorapidity (ETA) and momentum (P) in a well-populated phase-space region. |
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Weighted real-data and generated distributions of RichDLLmu for muon and pion track candidates in bins of pseudorapidity (ETA) and momentum (P) over full phase-space. |
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Weighted real-data and generated distributions of RichDLLp for proton and pion track candidates in bins of pseudorapidity (ETA) and momentum (P) over full phase-space. |
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Differences between real- and generated-sample areas under ROC-curves divided by uncertainties for discriminating kaons from pions, classifying with the RichDLLk variable, in bins of momentum and pseudorapidity. |
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Differences between real- and generated-sample areas under ROC-curves divided by uncertainties for discriminating muons from pions, classifying with the RichDLLmu variable, in bins of momentum and pseudorapidity. |
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Differences between real- and generated-sample areas under ROC-curves divided by uncertainties for discriminating protons from pions, classifying with the RichDLLp variable, in bins of momentum and pseudorapidity. |
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Differences between real- and generated-sample areas under ROC-curves for discriminating kaons from pions, classifying with the RichDLLk variable, in bins of momentum and pseudorapidity. |
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Differences between real- and generated-sample areas under ROC-curves for discriminating muons from pions, classifying with the RichDLLmu variable, in bins of momentum and pseudorapidity. |
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Differences between real- and generated-sample areas under ROC-curves for discriminating protons from pions, classifying with the RichDLLp variable, in bins of momentum and pseudorapidity. |
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Kaon identification efficiency and pion misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different \DeltaLLKPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively. |
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Proton identification efficiency and pion misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different \DeltaLLPPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively. |
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Proton identification efficiency and kaon misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different \DeltaLLPK requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively. |
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Muon identification efficiency and pion misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different identification requirements have been imposed on the samples, resulting in the open (isMuon) and filled marker distributions (\DeltaLLMuPi), respectively. |
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Muon identification efficiency and kaon misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different identification requirements have been imposed on the samples, resulting in the open (isMuon) and filled marker distributions (\DeltaLLMuK), respectively. |
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Muon identification efficiency and proton misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different identification requirements have been imposed on the samples, resulting in the open (isMuon) and filled marker distributions (\DeltaLLMuP), respectively. |
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Kaon identification efficiency and pion misidentification rate as measured using 2016 MagDown data as a function of track momentum. Two different \DeltaLLKPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively. |
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Kaon identification efficiency and pion misidentification rate as measured using 2016 MagUp data as a function of track momentum. Two different \DeltaLLKPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively. |
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Kaon identification efficiency and pion misidentification rate as measured using 2016 data (MagDown + MagUp) as a function of track momentum. Two different \DeltaLLKPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively. |
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Kaon identification efficiency and pion misidentification rate as measured using 2016 data with different \DeltaLLKPi requirements. |
Plot | Description |
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Muon identification efficiency and pion misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different identification requirements have been imposed on the samples, resulting in the open (isMuon) and filled marker distributions (\DeltaLLMuPi), respectively. |
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Muon identification efficiency and kaon misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different identification requirements have been imposed on the samples, resulting in the open (isMuon) and filled marker distributions (\DeltaLLMuK), respectively. |
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Muon identification efficiency and proton misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different identification requirements have been imposed on the samples, resulting in the open (isMuon) and filled marker distributions (\DeltaLLMuP), respectively. |
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Kaon identification efficiency and pion misidentification rate as measured using 2015 data as a function of track momentum. Two different \DeltaLLKPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively. |
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Proton identification efficiency and kaon misidentification rate as measured using 2015 data as a function of track momentum. Two different \DeltaLLPK requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively. |
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Proton identification efficiency and pion misidentification rate as measured using 2015 data as a function of track momentum. Two different \DeltaLLPPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively. |
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Kaon identification efficiency and pion misidentification rate as measured using 2012 data as a function of track momentum. Two different \DeltaLLKPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively. |
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Proton identification efficiency and kaon misidentification rate as measured using 2012 data as a function of track momentum. Two different \DeltaLLPK requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively. |
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Proton identification efficiency and pion misidentification rate as measured using 2012 data as a function of track momentum. Two different \DeltaLLPPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively. |
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Reconstructed Cherenkov angle for \emph{isolated} tracks, as a function of track momentum in the \cfourften radiator. The Cherenkov bands for muons, pions, kaons and protons are clearly visible. |
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Kaon identification efficiency and pion misidentification rate as measured using data as a function of track momentum. Two different \DeltaLLKPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively. |
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Kaon identification efficiency and pion misidentification rate from simulation as a function of track momentum. Two different \DeltaLLKPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively. |
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Pion misidentification fraction versus kaon identification efficiency as measured in 7\,TeV LHCb collisions as a function of track multiplicity. The efficiencies are averaged over all particle momenta. |
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Pion misidentification fraction versus kaon identification efficiency as measured in 7\,TeV LHCb collisions as a function of the number of reconstructed primary vertices. The efficiencies are averaged over all particle momenta. |
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Electron identification performance using the $\deltaLLCombepi$ variable, as measured in 8\,TeV collision data, using a tag and probe technique with electrons from the decay $B^{\pm} \to (J/\psi \to e^+e^-) K^{\pm}$. Pion misidentication rate versus electron identification probability when the cut value is varied. |
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Electron identification performance using the $\deltaLLCombepi$ variable, as measured in 8\,TeV collision data, using a tag and probe technique with electrons from the decay $B^{\pm} \to (J/\psi \to e^+e^-) K^{\pm}$. Electron identification efficiency and pion misidentification rate as a function of track momentum, for two different cuts on $\deltaLLCombepi$. |
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Background misidentification rates versus muon identification efficiency, as measured in the $\Sigma^+\to p\mu^+\mu^-$ decay study. The variables $\deltaLLXpi$ (black) and ProbNN (red), the probability value for each particle hypothesis, are compared for $5-10$\gevc muons and $5-50$\gevc protons, using data sidebands for backgrounds and Monte Carlo simulation for the signal. |
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Background misidentification rates versus proton identification efficiency, as measured in the $\Sigma^+\to p\mu^+\mu^-$ decay study. The variables $\deltaLLXpi$ (black) and ProbNN (red), the probability value for each particle hypothesis, are compared for $5-10$\gevc muons and $5-50$\gevc protons, using data sidebands for backgrounds and Monte Carlo simulation for the signal. |