TRACKING BY A MODIFIED ROTOR MODEL OF NEURAL-NETWORK

On the basis of applying neural networks to the track finding problem, investigations are made according to the specific properties of such discrete detectors as multiwire proportional chambers. These investigations result in the modification of the so-called rotor model of a neural network. The energy function of a network in this modification contains only one ''cost'' term. This speeds up calculations considerably. The reduction of the energy function is done by the neuron selection with the help of simple geometrical and energetical criteria. Besides, cellular automaton was applied to preliminary selection of data that made it possible to create an initial network configuration with the energy closer to its global minimum. We study also the following problems of track information extraction by our ANN model: providing an initial ANN configuration by an algorithm general enough to be applicable for any discrete detector in- or out of a magnetic field; robustness to heavy contaminated raw data; stability to the growing event multiplicity. The algorithm was tested on real and simulated events obtained from the ARES spectrometer with satisfactory results.