Title :
Standalone First Level Event Selection Package for the CBM Experiment
Author :
Kisel, Ivan ; Kulakov, Igor ; Zyzak, Maksym
Author_Institution :
Uni-Frankfurt - Goethe Univ., Frankfurt am Main, Germany
Abstract :
The main focus of the CBM experiment (FAIR, Germany) is the measurement of very rare probes at interaction rates of up to 10 MHz. The experiment will operate with a data flow of up to 1 TB/s and requires full online event reconstruction and selection. This is a task of the First Level Event Selection (FLES). A standalone FLES package has been developed for the CBM experiment. It contains modules for all reconstruction stages: track finding, track fitting, short-lived particle finding and selection. Reconstruction of about 50 types of particle decay channels is currently implemented. The algorithms are local with respect to data and their implementation is both vectorized (SIMD) and parallelized between CPU cores. For the track reconstruction the Cellular Automaton (CA) and the Kalman filter (KF) algorithms are used, that allows to achieve a high track reconstruction efficiency of up to 97% and a track parameters quality with 1% momentum resolution. The KF particle finder has a high efficiency with an optimal signal to background ratio. The FLES package shows a strong scalability on many-core systems and a processing speed of 1700 events per second on an Intel based computer with 80 cores. The investigation was done based on simulated minimum bias Au-Au UrQMD collisions at 25 AGeV with a realistic detector response.
Keywords :
Kalman filters; high energy physics instrumentation computing; image reconstruction; pattern recognition; Au-Au UrQMD collisions; CBM experiment; CPU cores; KF particle finder; Kalman filter algorithms; SIMD; cellular automaton; first level event selection; full online event reconstruction; many-core system; optimal signal-to-background ratio; particle decay channels; short-lived particle finding; standalone FLES package; standalone first level event selection package; track finding; track fitting; track reconstruction; Approximation methods; Detectors; Graphics processing units; Kalman filters; Registers; Vectors; Data processing; elementary particles; high performance computing; pattern recognition;
Journal_Title :
Nuclear Science, IEEE Transactions on
DOI :
10.1109/TNS.2013.2265276