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Towards Online Event Reconstruction in the CBM Experiment

K -.  +. Towards Online Event Reconstruction in the CBM Experiment. Tracking Challenge. Open Charm Event Selection. D  (c = 312 m): D +  K -  +  + (9.5%) D 0 (c = 123 m): D 0  K -  + (3.8%) D 0  K -  +  +  - (7.5%) D  s (c = 150 m):

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Towards Online Event Reconstruction in the CBM Experiment

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  1. K- + Towards Online Event Reconstruction in the CBM Experiment Tracking Challenge Open Charm Event Selection D (c = 312 m): D+  K-++(9.5%) D0 (c = 123 m): D0  K-+(3.8%) D0  K- + + -(7.5%) Ds (c = 150 m): D+s  K+K-+(5.3%) +c (c = 60 m): +c  pK-+(5.0%) No simple trigger primitive, like high pt, available to tag events of interest. The only selective signature is the detection of the decay vertex. First level event selection is done in a processor farm fed with data from the event building network  Fixed-target heavy-ion experiment  107 Au+Au collisions/sec  ~ 1000 charged particles/collision  Non-homogeneous magnetic field  Double-sided strip detectors  Track reconstruction in STS/MVD and displaced vertex search required in the first trigger level High Speed Tracking Algorithms on Modern and Future CPU/GPU Architectures Very efficient tracking algorithms are essential for the feasibility of the open charm event selection  Up to 109 tracks/sec in the Silicon Tracker  Develop algorithms which exploit the full potential of modern and future many-core processors. CA Track Finder KF Track Fitter Intel CPU 2x4 cores Central Au+Au reconstruction scalability on 2 Intel Xenon X5550 with 8 cores Relative momentum resolution obtained using Intel ArBB Intel MICA 32 cores Typical Au+Au collision reconstructed on 2 Intel Xenon X5550 with 8 cores Cooperationwith Intel Reliable Track Finding and Fitting Future Challenge: 4-Dimensional Tracking CA Track Finder DAF Track Fitter • Triggerless readout at extreme interaction rates: • no conventional event building, but processing of free-streaming data • Monte Carlo: • time-based detector simulation • Digitization: • model detector response as function of time • Reconstruction • cluster/hit finding based on time stamps • tracking: include time information in the CA track finder • event building based on time (hit level) and vertices (track level) Reconstruction efficiencies and ghost rate versus the detector strip efficiency Percentage of rejected hits depending on a hit displacement on the 4-th STS station Cooperationwith HEPHY (Austria), Uni-Oslo (Norway), LIT/JINR (Russia)

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