3D Reconstruction of 10000 Particle Trajectories in Real-time
J. Alme1, S. Bablok1, M. Richter1, D. Röhrich1, K. Ullaland1, B. Wagner1, G. Øvrebekk1, K. Fanebust Hetland2, H. Helstrup2, K. Røed2, K. Aamodt3, B. Skaali3, T. Tveter3, T. Alt4, V. Lindenstruth4, T. Steinbeck4, J. Thäder4, H. Appelshäuser5, M. Ploskon5
1 Department of Physics and Technology, University of Bergen, Norway, Email: Dieter.Rohrich@ift.uib.no
2Faculty of Engineering, Bergen University College, Norway
3Department of Physics, University of Oslo, Norway
4Kirchhoff-Institute for Physics, University of Heidelberg, Germany
5Institute of Nuclear Physics, University of Frankfurt, Germany
Tracking detectors in high-energy physics experiments produces hundreds of megabytes of data at a rate of several kHz. Processing this data at a bandwidth of 10-20 GB/sec requires massive computing. The prime task is to reduce the data rate to a manageable amount by real time data compression and pattern recognition techniques. Clustered SMP (Symmetric Multi-Processor) nodes, based on off-the- shelf PCs, connected by a high bandwidth network and running a standard operating system (LINUX), provide the necessary computing power. Such a system can easily be interfaced to the front-end electronics of the detectors via the internal PCI-bus. The High Level Trigger of e.g. the ALICE experiment at the LHC/CERN combines and processes the full information from all major detectors in a large computer cluster. Data rate reduction is achieved by reducing the event rate by selecting interesting events (software trigger) and by advanced data compression. Track reconstruction chains for analysing events containing more than 10000 tracks have been benchmarked.
Keywords 3D tracking detectors, real-time processing, pattern recognition
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