Jet Algorithm Comparisons Gen

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Jet Algorithm Comparisons- Gen Reco results -

• Agata Smoron
• Lenny Apanasevich, Marek Zielinski
• 26 July, 2006
• University of Illinois at Chicago

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Introduction

• Analysis uses QCD 2 2 hard scatters generated
  by PYTHIA in CMSSW_0_7_2
• 120 lt pT lt 170
• Jets are defined using
• Midpoint-cone algorithm with ?R lt 0.5 (Mcone5)
• Iterative-cone algorithm with ?R lt 0.5 (Icone5)
• Midpoint-cone algorithm with ?R lt 0.7 (Mcone7)
• Inclusive kT algorithm (with R parameter 1.0)

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Generated results
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Reconstructed results
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? lt 2.5
ICone5_MCone5
Gen Jets
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ICone5_MCone5
Rec Jets
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2 Leading
Gen Jets
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2 Leading
Rec Jets
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Iguana Lego Display in CMSSW_0_7_0 (700 GeV Z g
Dijets)
Two Jets, No Merging
Iterative Cone R0.5 Jets
Calo Towers Lego Display
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One Merged Jet
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Midpoint Cone R0.5 Jets
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• Jets you get depend on the algorithm
• Midpoint cone doing better job here.
• Merging finds leading dijet in Z event.
• May not do better job for all physics.

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Distance to nearest jet
Generated
Reconstructed
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Comparisons between matched Jets
Generated
Reconstructed
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Jet finding efficiency
RCUT
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ET Resolution with Gaussian FitAll Jets ? lt
1.4, cone Radius 0.2, 40 lt ETGen lt 60
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Resolution
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Conclusion

• All the algorithms seem to work
• ICone5 jets tend to contain lower energy than
  MCone5 jets, especially at small ET
• ICone5 is better at finding jets at low ET than
  MCone5
• As of today CMSSW shows worse resolution than
  ORCA
• We plan to do similar comparisons with KT
  algorithm