## 2 Related Works and the Problem Formulation  
We start our overview with two articles that have influenced research on the development of deep tracking algorithms, conducted at JINR in collaboration with al-Farabi Kazakh National University. The paper [7] presents deep learning methods for the reconstruction of particle tracks in high-energy physics experiments, which is a critical task for determining the type and energy of the obtained particles. The proposed methods are aimed at improving the existing methods of track reconstruction by using the capabilities of neural networks. The authors presented a type of neural network, called a \"directed message network\", which is designed to work with graph structures, such as those commonly used to represent particle detector data. A directed message network is capable of multi-step message passing and aggregation operations, which allow it to gather information from neighboring graph nodes and incorporate it into its predictions. This article discusseddeep learning techniques that can significantly improve the performance of particle track reconstruction in HEP experiments. Another project the main aim of which is to study the applicability of deep learning methods to the problem of track reconstruction is Exa.TrkX [8]. Researchers from Exa.TrkX have developed the Geometric Deep Learning Pipeline for HL-LHC Particle Tracking. The pipeline is based on graph neural networks (GNNs) that work with event data represented in the form of an acyclic directed graph where the nodes are hits connected between adjacent detector layers. The authors of [8] reached a technical efficiency (recall) of 91% with the precision value at 58.3% on the Monte-Carlo simulation data of top quark pair production from proton-proton collisions at the HL-LHC. The average processing speed of one event on a single Nvidia V100 GPU is 3.3 s [8]. Nevertheless, it is important to note that the articles mentioned above did not address the significant challenge of tracking particles in detectors such as the silicon vertex detector and straw tube-based tracking system, which are components of the SPD setup. The high level of background noise produced in these detectors not only complicates the tracking process itself but also results in a substantial number of false tracks. Moreover, in the worst case, the number of such fake hits grows quadratically in proportion to the number of particle tracks. Track building in a noisy environment often leads to the production of many false tracks that may consist of fragments of multiple real tracks or noise hits. Thereby, there are three main metrics for the evaluation of the track building model: reconstruct as many real tracks as possible (recall); the lower the number of false tracks that the model reconstructed as potentially real tracks, the better (precision); and the throughput of the algorithm should be as high as possible (processing speed). Finding the algorithm that maximizes all three metrics is essential for the fast and accurate track reconstruction process. The neural network designed to deal with highly contaminated data was proposed in [9], as the TrackNET neural model. It receives as input the coordinates of points of track candidates used as seeds and predicts the center and semi-axes of the ellipse on the next coordinate plane, on which the candidate track continuation is searched. If any hits fall within the ellipse, several of them are selected with a K-nearest neighbors search. Selected hits are appended to the track candidate to prolong it and produce K new track candidates. After all the mentioned steps, the model takes newly created seeds as inputs and repeats the prediction. The procedure continues until there are no more stations or the candidate track leaves the detector area. If no hits fall within the ellipse, the track is not prolonged and is either moved from the seeds set to the finished track candidates set or dropped out as a too-short candidate if the number of hits does not exceed four.