The Compressed Baryonic Matter (CBM) experiment at the future FAIR facility will investigate the QCD phase diagram at high net-baryon densities and moderate temperatures. One of the key signatures will be di-leptons emitted from the hot and dense phase in heavy-ion collisions. Measuring di-electrons, a high purity of identified electrons is required in order to suppress the background. Electron identification in CBM will be performed by a Ring Imaging Cherenkov (RICH) detector and Transition Radiation Detectors (TRD). In order to access the foreseen rare probes, the detector and the data acquisition have to handle interaction rates up to 10MHz. Therefore, the development of fast and efficient event reconstruction algorithms is an important and challenging task in CBM. In this contribution event reconstruction and electron identification algorithms in the RICH detector are presented. So far they have been developed on simulated data but could already be tested on real data from a RICH prototype testbeam experiment at the CERN-PS. Efficient and fast ring recognition algorithms in the CBM-RICH are based on the Hough Transform method. Due to optical distortions of the rings, an ellipse fitting algorithm was elaborated to improve the ring radius resolution. An efficient algorithm based on the Artificial Neural Network was implemented for electron identification in RICH. All algorithms were significantly optimized to achieve maximum speed and minimum memory consumption.
Financed by the National Centre for Research and Development under grant No. SP/I/1/77065/10 by the strategic scientific research and experimental development program:
SYNAT - “Interdisciplinary System for Interactive Scientific and Scientific-Technical Information”.