4.2 Validationplots for Di-Jet Sample

These jobs were run on the sample:

calib0_csc11.005014.J5_pythia_jetjet.recon.AOD.v12003104_tid00417

Compared are the ET values of KtTowerJet particles with the respective truth jets, with a matching cut in DeltaR<=0.2 The sample is split up in bins of eta, phi and energy (see values in MeV below)

JOBOPTION-File:

HadCalibValidation_AOD.JetContainer = "KtTowerParticleJets"

HadCalibValidation_AOD.JetTruthContainer = "KtTruthParticleJets"

HadCalibValidation_AOD.EnergyVariable = "Et"

HadCalibValidation_AOD.NumberEtaBins = 4

HadCalibValidation_AOD.NumberEnergyBins = 4

HadCalibValidation_AOD.NumberPhiBins = 4

HadCalibValidation_AOD.EtaBins = [ 0.0, 0.75, 1.5, 2.5, 9.9 ]

HadCalibValidation_AOD.PhiBins = [ -3.5, -1.5, 0.0, 1.5, 3.5 ]

HadCalibValidation_AOD.EnergyBins = [ 0.0 , 100000. , 200000. , 500000. , 900000000. ]

HadCalibValidation_AOD.DeltaRMatchCut = 0.2

Fig. 4.2.1: Resolution in bins of Phi and ET

Plotted is ET(RecoJet) - ET(TrueJet) / ET(TrueJet). The variable used then later for determining the linearity is sigma/(mean+1), the values for each fit are given in the statistic box with the slightly confusing name (sigma / E and RMS / E respectively, but it is indeed Sigma / (Mean +1)

Phi_Et_Resolution.eps

Fig. 4.2.2: Resolution in bins of Eta and ET

Eta_Et_Resolution.eps

4.2.1 Linearity and Resolution depending on Phi

Fig. 4.2.3: Linearity (from Gaussian Fit)

Fig. 4.2.4: Linearity (from Histogramm Mean)

Fig. 4.2.5: Resolution (from Histogramm Mean)

Fig. 4.2.6: Resolution (from Histogramm Mean)

4.2.2 Linearity and Resolution depending on Eta

Fig. 4.2.7: Linearity (from Gaussian Fit)

Fig. 4.2.8: Linearity (from Histo mean)

Fig. 4.2.9: Resolution (from Gaussian Fit)

Fig. 4.2.10: Resolution (from Histo mean)

-- Main.kristin - 12 Feb 2007