TWiki> LHCb Web>LHCbPPTS>PIDConferencePlots (2020-06-12, MichaelAlexander) EditAttachPDF

PID Plots for Conference

Run 2

Muon ID with MVAs

Contact: Nikita Kazeev

Plot Description
plots_for_approval_plots_acat_full_cv_ROCs_DSt_Pi.png

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

plots_for_approval_plots_acat_full_cv_ROCs_Lam0LL_P.png

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

plots_for_approval_plots_run_2_ROCs_DSt_Pi.png

Muon efficiency vs pion rejection after applying IsMuon&IsMuonUnbiased&PT>=800 selection, no kinematic reweighting, 2016 calibration data

Fast DLL simulation

Contact: Artem Maevskiy

Plot Description
P_vs_ETA.png

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: $J/\psi\rightarrow\mu^+\mu^-$, $D^{+*}\rightarrow D^0(K^-\pi^+)\pi^+$, $K^0_S\rightarrow\pi^+\pi^-$, $D^+_s\rightarrow\phi(K^+K^-)\pi^+$ and $\Lambda^0\rightarrow p\pi^-$.
kaon_vs_pion_RichDLLk_in_Brunel_P_vs_Brunel_ETA_3x3_full.png

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.
kaon_vs_pion_RichDLLk_in_Brunel_P_vs_Brunel_ETA_8x8_S0_2_4_N_S1_2_4_N.png

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.
muon_vs_pion_RichDLLmu_in_Brunel_P_vs_Brunel_ETA_3x3_full.png

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.
proton_vs_pion_RichDLLp_in_Brunel_P_vs_Brunel_ETA_3x3_full.png

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.
DeltaAUCOverError_RichDLLk_kaon_vs_all.png

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.
DeltaAUCOverError_RichDLLmu_muon_vs_all.png

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.
DeltaAUCOverError_RichDLLp_proton_vs_all.png

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.
DeltaAUC_RichDLLk_kaon_vs_all.png

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.
DeltaAUC_RichDLLmu_muon_vs_all.png

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.
DeltaAUC_RichDLLp_proton_vs_all.png

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.

Deuteron ID

*REPLACED BY LHCb-FIGURE-2020-013*

2017

RICH

Plot Description
Plot_2017Validation_KPi_P.png 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.
Plot_2017Validation_PPi_P.png 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.
Plot_2017Validation_PK_P.png 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.

MUON

Plot Description
Plot_2017Validation_MuPi_P.png 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.
Plot_2017Validation_MuK_P.png 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.
Plot_2017Validation_MuP_P.png 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.

2016

RICH

Plot Description
MagDown_2016.png 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.
MagUp_2016.png 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.
Merged_2016.png 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.
ROC_2016.png Kaon identification efficiency and pion misidentification rate as measured using 2016 data with different \DeltaLLKPi requirements.

MUON

Plot Description
Plot_2016_MuPi_P.png 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.
Plot_2016_MuK_P.png 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.
Plot_2016_MuP_P.png 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.

2015

RICH

Plot Description
S23_KPi_P.png 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.
S23_PK_P.png 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.
S23_PPi_P.png 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.

MUON

S23_MuMu_MagDown.png Efficiency of the isMuon selection based on the matching of hits in the muon system to track extrapolation as a function of momentum (black) and efficiency of the isMuon selection plus \DeltaLL(\mu-h) >0, where h can be a pion (red), a kaon (blue) and a proton (green).
S23_PiMu_MagDown.png Misidentification probability of pions as a function of momentum after isMuon (black) and isMuon+\DeltaLL(\mu - \pi) >0 (red).
S23_KMu_MagDown.png Misidentification probability of kaons as a function of momentum after isMuon (black) and isMuon+\DeltaLL(\mu - K) >0 (blue).
S23_PMu_MagDown.png Misidentification probability of protons as a function of momentum after isMuon (black) and isMuon+\DeltaLL(\mu - P) >0 (green).

CALO

ePIDVsPiMisID2015.png Electron identification efficiency and pion misidentification rate as measured using 2015 data as a function of track momentum. Two different \DeltaLLePi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively. The plot is made only for electron track for which no bremsstrahlung photons have been recovered.

Run 1

2012

RICH

Plot Description
S20_KPi_P.png 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.
S20_PK_P.png 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.
S20_PPi_P.png 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.

2011

RICH

See the link to the RICH page. In addition the following plots are taken from LHCb Detector performance

Plot Description
Fig38.png 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.
Fig39left.png 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.
Fig39right.png 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.
Fig40left.png 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.
Fig40right.png 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.

CALO

The following plots are taken from the LHCb Detector performance

Plot Description

Fig44.png Comparison of the Monte Carlo performance of the gamma/pi0 separation tool developed by Yandex (full lines) and the default one (dashed line). The curves display the photon selection efficiency versus the pi0 selection efficiency as determined on a photon sample (B -> K*gamma) and on two pi0 samples (B -> Kpipi0 in blue and B -> J/psi K*[Kpi0] in green). The red dots indicate 95% photon efficency and the corresponding efficiency for pi0. The figure is taken from the proceedings of the ACAT 2017 conference.
Fig32.png Performance of the photon identification. Purity as a function of efficiency for (green) the full photon candidate sample, (blue) converted candidates according to the SPD information and (red) non-converted candidates (left). Photon identification efficiency as a function of \piz rejection efficiency for the $\gamma-\piz$ separation tool for simulation, the red curve, and data, the blue curve (right).
Fig35.png Electron identification efficiency versus misidentification rate.
Fig36.png Electron identification performances for various $\Delta$ log ${\mathcal L}^\text{CALO}(e-h)$ cuts: electron efficiency (left) and misidentification rate (right) as functions of the track momentum.

MUON

See the link to the MUON page

Fig41topleft.png Efficiency of the muon candidate selection based on the matching of hits in the muon system to track extrapolation as a function of momentum for different \pt ranges.
Fig41topright.png Misidentification probability of protons as a function of momentum for different \pt ranges.
Fig41bottomleft.png Misidentification probability of pions as a function of momentum for different \pt ranges.
Fig41bottomright.png Misidentification probability of kaons as a function of momentum for different \pt ranges.

Global

The following plots are taken from the LHCb Detector performance

Plot Description
Fig42left.png 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.
Fig42right.png 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 $\deltaLLCombepi$.
Fig43left.png 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.
Fig43right.png 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.
-- MariannaFontana - 2016-04-07

  • nPVs.pdf: nPVs spectrum used for traning and evaluating "Dev Catboost" model for Muon ID
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PDFpdf DeltaAUCOverError_RichDLLk_kaon_vs_all.pdf r1 manage 29.1 K 2019-04-11 - 12:46 MichaelAlexander  
PNGpng DeltaAUCOverError_RichDLLk_kaon_vs_all.png r1 manage 247.2 K 2019-04-11 - 12:46 MichaelAlexander  
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PNGpng DeltaAUC_RichDLLp_proton_vs_all.png r1 manage 291.6 K 2019-04-11 - 12:46 MichaelAlexander  
PDFpdf Fig32.pdf r1 manage 36.5 K 2016-04-11 - 11:22 MariannaFontana  
PNGpng Fig32.png r1 manage 96.7 K 2016-04-11 - 12:10 MariannaFontana  
PDFpdf Fig35.pdf r1 manage 38.3 K 2016-04-11 - 15:04 MariannaFontana  
PNGpng Fig35.png r1 manage 91.1 K 2016-04-11 - 15:04 MariannaFontana  
PDFpdf Fig36.pdf r1 manage 98.9 K 2016-04-11 - 15:04 MariannaFontana  
PNGpng Fig36.png r1 manage 159.8 K 2016-04-11 - 15:04 MariannaFontana  
PDFpdf Fig38.pdf r1 manage 74.9 K 2016-04-11 - 15:04 MariannaFontana  
PNGpng Fig38.png r1 manage 1067.9 K 2016-04-11 - 15:04 MariannaFontana  
PDFpdf Fig39left.pdf r1 manage 54.2 K 2016-04-11 - 15:45 MariannaFontana  
PNGpng Fig39left.png r1 manage 118.7 K 2016-04-11 - 15:45 MariannaFontana  
PDFpdf Fig39right.pdf r1 manage 55.2 K 2016-04-11 - 15:45 MariannaFontana  
PNGpng Fig39right.png r1 manage 123.2 K 2016-04-11 - 15:45 MariannaFontana  
PDFpdf Fig40left.pdf r1 manage 18.5 K 2016-04-11 - 15:04 MariannaFontana  
PNGpng Fig40left.png r1 manage 117.7 K 2016-04-11 - 15:04 MariannaFontana  
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PDFpdf Fig41bottomleft.pdf r1 manage 53.0 K 2016-04-11 - 16:25 MariannaFontana  
PNGpng Fig41bottomleft.png r1 manage 153.4 K 2016-04-11 - 16:25 MariannaFontana  
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PDFpdf Fig41topleft.pdf r1 manage 60.5 K 2016-04-11 - 16:25 MariannaFontana  
PNGpng Fig41topleft.png r1 manage 139.4 K 2016-04-11 - 16:25 MariannaFontana  
PDFpdf Fig41topright.pdf r1 manage 87.6 K 2016-04-11 - 16:25 MariannaFontana  
PNGpng Fig41topright.png r1 manage 145.7 K 2016-04-11 - 16:25 MariannaFontana  
PDFpdf Fig42left.pdf r1 manage 15.9 K 2016-04-11 - 15:05 MariannaFontana  
PNGpng Fig42left.png r1 manage 65.8 K 2016-04-11 - 15:05 MariannaFontana  
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PNGpng Fig42right.png r1 manage 113.5 K 2016-04-11 - 15:05 MariannaFontana  
PDFpdf Fig43left.pdf r1 manage 22.3 K 2016-04-11 - 15:05 MariannaFontana  
PNGpng Fig43left.png r1 manage 107.8 K 2016-04-11 - 15:05 MariannaFontana  
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PNGpng Fig43right.png r1 manage 106.5 K 2016-04-11 - 15:05 MariannaFontana  
PNGpng Fig44.png r1 manage 57.8 K 2017-11-09 - 10:23 MaxChefdeville  
PDFpdf FitToProbNNd_toy_25_29700_35400.pdf r3 r2 r1 manage 20.9 K 2019-03-28 - 15:24 SophieKatherineBaker  
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PDFpdf MagDown.pdf r1 manage 17.0 K 2016-10-03 - 17:13 WenbinQian  
PDFpdf MagDown_2016.pdf r1 manage 17.0 K 2016-10-03 - 21:06 MariannaFontana  
PNGpng MagDown_2016.png r1 manage 97.4 K 2016-10-03 - 21:06 MariannaFontana  
PDFpdf MagUp.pdf r1 manage 17.0 K 2016-10-03 - 17:13 WenbinQian  
PDFpdf MagUp_2016.pdf r1 manage 17.0 K 2016-10-03 - 21:06 MariannaFontana  
PNGpng MagUp_2016.png r1 manage 97.0 K 2016-10-03 - 21:06 MariannaFontana  
PDFpdf Merged.pdf r1 manage 17.0 K 2016-10-03 - 17:13 WenbinQian  
PDFpdf Merged_2016.pdf r1 manage 17.0 K 2016-10-03 - 21:07 MariannaFontana  
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PDFpdf P_vs_ETA.pdf r1 manage 193.2 K 2019-04-11 - 12:47 MichaelAlexander  
PNGpng P_vs_ETA.png r1 manage 642.5 K 2019-04-11 - 12:46 MichaelAlexander  
PDFpdf Plot_2016_MuK_P.pdf r1 manage 17.0 K 2017-09-19 - 16:04 MariannaFontana  
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PNGpng Plot_2017Validation_KPi_P.png r1 manage 104.5 K 2017-09-19 - 15:42 MariannaFontana  
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PDFpdf ROC.pdf r1 manage 14.2 K 2016-10-03 - 17:13 WenbinQian  
PDFpdf ROC_2016.pdf r1 manage 14.2 K 2016-10-03 - 21:07 MariannaFontana  
PNGpng ROC_2016.png r1 manage 85.4 K 2016-10-03 - 21:07 MariannaFontana  
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PDFpdf S23_KMu_MagDown.pdf r1 manage 14.8 K 2016-10-07 - 10:09 MariannaFontana  
PNGpng S23_KMu_MagDown.png r1 manage 94.2 K 2016-10-07 - 10:09 MariannaFontana  
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PDFpdf approved_AllParticles_Efficiency_DLLd_30_HasRICH.pdf r1 manage 17.8 K 2018-09-18 - 16:45 JeanFrancoisMarchand  
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PDFpdf ePIDVsPiMisID2015.pdf r1 manage 16.4 K 2017-05-08 - 15:01 ThibaudHumair  
PNGpng ePIDVsPiMisID2015.png r1 manage 16.6 K 2017-05-08 - 14:56 ThibaudHumair e ID and pi mis-ID rates vs momentum
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