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.