CEPC Tracking Performance Overview
Explore the tracking performance at the CEPC Baseline Detector, focusing on efficiency, resolution, and software frameworks. Delve into the essential role of tracking in reconstructing particle trajectories for the precise Higgs program. Discover the tracking methodologies such as ArborTracking and Clupatra for optimal results.
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Presentation Transcript
Tracking Performance at the Tracking Performance at the CEPC Baseline CEPC Baseline D Detector etector Mingrui Zhao China Institute of Atomic Energy Nov. 12. 2018 CEPC International Workshop
Contents Introduction to CEPC tracking Framework and software Efficiency and resolution performances Conclusion 2
Introduction CEPC: A precise Higgs program Track: Essential object in reconstruction Represent the trajectory of the particle Provide momentum information Provide input to PFA Critical requirements for tracking 3
Overview of tracking at baseline detector(TPC) Marlin KalTest MarlinTrk ArborTracking Clupatra 4
Clupatra Neareast neighbour type clustering Designed for ILD Work very well for CEPC TPC 5
ArborTracking Tree configuration clustering Designed for CEPC Comparable performance as clupatra 6
Efficiency study Efficiency =?Reco Reference set Tracks that should be reconstructed ?Ref Matching Digi-Hit, MCParticle match: Sim-Hit from the MCParticle contributes to the Digi-Hit Track, MCParticle match: #(hits from MCParticle)/#(hits to reconstruct track) > 0.9 7
Single-muon efficiency Sample: particle gun ArborTracking Clupatra 8
3-prong efficiency Sample: Z 3 + inclusive Minimal angle: Minimal angle between the pions in the events Efficiency =# of reconstructed events Reconstructed: All pions in the event are reconstructed. # of events 9
Single track resolution Sample: particle gun ArborTracking Clupatra 10
PFA performance Sample: e+e (H gg) ArborTracking Clupatra 11
Conclusion Satisfy the requirement of CEPC ArborTracking behaves well as Clupatra More work to do 12
