Computational Challenges for the CBM Experiment
CBM (“Compressed Baryonic Matter”) is an experiment being prepared to operate at the future Facility for Anti-Proton and Ion Research (FAIR) in Darmstadt, Germany, from 2018 on. CBM will explore the high-density region of the QCD phase diagram by investigating nuclear collisions from 2 to 45 GeV beam energy per nucleon. Its main focus is the measurement of very rare probes (e.g. charmed hadrons), which requires interaction rates of up to 10 MHz, unprecedented in heavy-ion experiments so far. Together with the high multiplicity of charged tracks produced in heavy-ion collisions, this leads to huge data rates (up to 1 TB/s), which must be reduced on-line to a recordable rate of about 1 GB/s.
Moreover, most trigger signatures are complex (e.g. displaced vertices of open charm decays) and require information from several detector sub-systems. The data acquisition is thus being designed in a free-running fashion, without a hardware trigger. Event reconstruction and selection will be performed on-line in a dedicated processor farm. This necessitates the development of fast and precise reconstruction algorithms suitable for on-line data processing. In order to exploit the benefits of modern computer architectures (many-core CPU/GPU), such algorithms have to be intrinsically local and parallel and thus require a fundamental redesign of traditional approaches to event data processing. Massive hardware parallelisation has to be reflected in mathematical and computational optimisation of the algorithms. This is a challenge not only for CBM, but also for current and future experiments, in particular for heavy-ion eperiments like e.g. ALICE at the LHC.
For the development of the proper algorithms, a careful simulation of the input data is required. Such a simulation must reflect the free-running DAQ concept, where data are delivered asynchronously by the detector front-ends on activation, and no association to a physical interaction is given a priori by a hardware trigger. It hence goes beyond traditional event-based software frameworks. In this article, we present the challenges of and the current approaches to simulation, data processing and reconstruction in the CBM experiment.
KeywordsEvent Reconstruction Charged Track Open Charm Displace Vertex Transition Radiation Detec
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- 1.Al-Turany, M., Uhlig, F.: The FAIRROOT framework. PoS(ACAT08)048 (2008)Google Scholar
- 3.CBM Experiment, http://www.gsi.de/forschung/fair_experiments/CBM/index_e.html
- 4.CBM Experiment, Technical Status Report, Darmstadt (2005)Google Scholar
- 5.De Cuveland, J.: A first-level event selector for the CBM experiment at FAIR. In: Proceedings of CHEP 2010 (2010)Google Scholar
- 6.Facility for Antiproton and Ion Research, http://www.fair-center.de
- 7.FAIR Conceptual Design Report, Darmstadt (2001)Google Scholar
- 8.FAIR Baseline Technical Report, Darmstadt (2006) ISBN 3-9811298-0-6Google Scholar
- 9.Friese, V.: The CBM experiment at FAIR. PoS(CPOD07)056 (2007)Google Scholar
- 14.Kisel, I.: Many-core scalability of the online event reconstruction in the CBM experiment. In: Proceedings of CHEP 2010 (2010)Google Scholar
- 15.Lebedev, S.: Algorithms and Software for Event Reconstruction in the RICH, TRD and MUCH Detectors of the CBM Experiment. In: Adam, G., Buša, J., Hnatič, M. (eds.) MMCP 2011. LNCS, vol. 7125, pp. 246–251. Springer, Heidelberg (2012)Google Scholar
- 16.Senger, P.: FAIR/CBM capabilities for charm and dilepton studies. PoS(CPOD 2009)042 (2009)Google Scholar