Study of BESⅢ MUC offine software with cosmic-ray data

Cosmic-ray data of 90 M events have been collected and used for calibration,alignment as well as detector tuning. A special tracking algorithm for the BESⅢ muon counter is developed and verified with Monte-Carlo simulation and then further confirmed with the cosmic-ray data. The obtained strip resolutions are in good agreement with the design values. A new alignment approach for the BESⅢ muon counter is confirmed with the cosmic-ray data and proposed to be used in future analysis of experimental data.

A method for interpolating asymmetric peak shapes in multiplet γ-ray spectra

The peak shapes ofT-rays at various energies must be known before unfolding the multiplet spectra obtained by using semiconductor or scintillation detectors. Traditional methods describe isolated peaks with multi-parameter fitting functions, and assume that most of these parameters do not vary with energy because it is rare to find a spectrum with enough isolated peaks to constrain their dependence. We present an algorithm for interpolating the T-ray profile at any intermediate energy given a pair of isolated T-ray peaks from the spectrum under consideration. The algorithm is tested on experimental data and leads to a good agreement between the interpolated profile and the fitting function. This method is more accurate than the traditional approach, since all aspects of the peak shape are allowed to vary with energy. New definitions of Left-Half Width at Half Maximum, and Right-Half Width at Half Maximum for peak shape description are introduced in this paper.

An artificial neural network for proton identification in HERMES data

The HERMES time-of-flight （TOF） system is used for proton identification, but must be carefully calibrated for systematic biases in the equipment. This paper presents an artificial neural network （ANN） trained to recognize protons from ∧^0 decay using only raw event data such as time delay, momentum, and trajectory. To avoid the systematic errors associated with Monte Carlo models, we collect a sample of raw experimental data from the year 2000. We presume that when for a positive hadron （assigned one proton mass） and a negative hadron （assigned one π^- mass） the reconstructed invariant mass lies within the ∧^0 resonance, the positive hadron is more likely to be a proton. Such events are assigned an output value of one during the training process; all others were assigned the output value zero. The trained ANN is capable of identifying protons in independent experimental data, with an efficiency equivalent to the traditional TOF calibration. By modifying the threshold for proton identification, a researcher can trade off between selection efficiency and background rejection power. This simple and convenient method is applicable to similar detection problems in other experiments.

Track reconstruction in the BESⅢ muon counter

The reconstruction algorithm for BESⅢ Muon Counter, MucRecAlg, is developed with the objectoriented language C＋＋ in BESⅢ offine software environment. MucRecAlg consists of the following functions：to find track seeds either from extrapolation of tracks in the main drift chamber or from the fired strips in muon counter, to select fired strips associated to the candidate tracks, to fit the candidate tracks with a linear or quadratic function and to calculate other parameters of the tracks for muon identification. Monte Carlo samples are generated to check the performance of the reconstruction package, such as reconstruction effciency, muon remaining rate and pion rejection rate, etc. The preliminary results show that the pion rejection rate is around 3%—4% while the muon remaining rate is better than 90% in 0.4—1.6 GeV/c momentum region, which meets the requirement as shown in the design report.