Результаты поиска по 'GEM detectors':
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  1. Ososkov G.A., Bakina O.V., Baranov D.A., Goncharov P.V., Denisenko I.I., Zhemchugov A.S., Nefedov Y.A., Nechaevskiy A.V., Nikolskaya A.N., Shchavelev E.M., Wang L., Sun S., Zhang Y.
    Tracking on the BESIII CGEM inner detector using deep learning
    Computer Research and Modeling, 2020, v. 12, no. 6, pp. 1361-1381

    The reconstruction of charged particle trajectories in tracking detectors is a key problem in the analysis of experimental data for high energy and nuclear physics.

    The amount of data in modern experiments is so large that classical tracking methods such as Kalman filter can not process them fast enough. To solve this problem, we have developed two neural network algorithms of track recognition, based on deep learning architectures, for local (track by track) and global (all tracks in an event) tracking in the GEM tracker of the BM@N experiment at JINR (Dubna). The advantage of deep neural networks is the ability to detect hidden nonlinear dependencies in data and the capability of parallel execution of underlying linear algebra operations.

    In this work we generalize these algorithms to the cylindrical GEM inner tracker of BESIII experiment. The neural network model RDGraphNet for global track finding, based on the reverse directed graph, has been successfully adapted. After training on Monte Carlo data, testing showed encouraging results: recall of 98% and precision of 86% for track finding.

    The local neural network model TrackNETv2 was also adapted to BESIII CGEM successfully. Since the tracker has only three detecting layers, an additional neuro-classifier to filter out false tracks have been introduced. Preliminary tests demonstrated the recall value at the first stage of 99%. After applying the neuro-classifier, the precision was 77% with a slight decrease of the recall to 94%. This result can be improved after the further model optimization.

    Keywords: track reconstruction, GEM detectors, deep learning, convolutional neural networks, graph neural networks.

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