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At present, the accelerator complex NICA is being built at JINR (Dubna). It is intended for performing experiments to study interactions of relativistic nuclei and polarized particles (protons and deuterons). One of the experimental facilitues MPD (MultiPurpose Detector) was designed to investigate nucleus-nucleus, protonnucleus and proton-proton interactions. The existing plans of future MPD upgrade consider a possibility to install an inner tracker made of the new generation silicon pixel sensors. It is expected that such a detector will considerably enhance the research capability of the experiment both for nucleus-nucleus interactions (due to a high spatial resolution near the collision region) and proton-proton ones (due to a fast detector response).

This paper presents main characteristics of such a tracker, obtained using a Monte-Carlo simulation of the detector for proton-proton collisions. In particular, the detector ability to reconstruct decay vertices of short-lived particles and perform a selection of rare events of such decays from much more frequent “common” interactions are evaluated. Also, the problem of a separation of multiple collisions during the high luminosity accelerator running and the task of detector triggering on rare events are addressed. The results obtained can be used to justify the necessity to build such a detector and to develop a high-level trigger system, possibly based on machine learning techniques.

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.