Tracking is an important task in the field of High Energy physics. Modern experiments produceenormous amounts of data, and classical tracking algorithms cannot reach required computingefficiency. This lead to the need to develop new methods, some of them use neural network models.In our work we present modifications of previously developed model, TrackNetV2. This model and itsdescendants showed great results for Monte-Carlo simulations of experiments with microstrip-basedGEM detectors: BESIII and BM@N RUN6. In this work we adapt it to more complex scenario forBM@N RUN7. The work showed limitations in architecture and training procedure, which arereworked later.
Particle tracking is an essential part of any high-energy physics experiment. Well-known tracking algorithms based on the Kalman filter are not scaling well with the amounts of data being produced in modern experiments. In our work we present a particle tracking approach based on deep neural networks for the BM@N experiment and future SPD experiment. We have already applied similar approaches for BM@N RUN 6 and BES-III Monte-Carlo simulation data. This work is the next step in our ongoing study of tracking with the help of machine learning. Revised algorithms -combination of Recurrent Neural Network (RNN) and Graph Neural Network (GNN) for the BM@N RUN 7 Monte-Carlo simulation data, and GNN for the preliminary SPD Monte-Carlo simulation data are presented. Results of the track efficiency and processing speed for both experiments are demonstrated.
The SPD (Spin Physics Detector) is a planned spin physics experiment in the second interaction pointof the NICA collider that is under construction at JINR. The main goal of the experiment is the test ofbasic of the QCD via the study of the polarized structure of the nucleon and spin-related phenomena inthe collision of longitudinally and transversely polarized protons and deuterons at the center-of-massenergy up to 27 GeV and luminosity up to 1032 1/(cm2 s). The data rate at the maximum designluminosity is expected to reach 0.2 Tbit/s. Current approaches to SPD computing and offline softwarewill be presented. The plan of the computing and software R&D in the scope of the SPD TDRpreparation will be discussed.
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