## 4 Results and Discussion
### False Track Rejection with Contaminated Data  
Due to bias in the original dataset, the model has some limitations in terms of the potentially poor reconstruction of true tracks in the presence of highly noisy data that contain a lot of false hits. Actual experimental data show that, on average, the number of false hits in each detector can reach up to \(n\) to \(n^{2}\), where \(n\) represents the total number of tracks per event. Therefore, to test the introduced criteria for track clustering based on the chi-squared measure, we propose modeling more realistic samples and adding noisy hits to the dataset, corresponding to two different noise levels. The first noise level will contain 100 false hits per station, while the higher noise level will contain 1000 false hits. The comparative clustering results are presented in Table 3.  
The model takes a pandas DataFrame object [18] containing information on events, with columns for \(x\), \(y\), \(z\), the event number, the station number, and the track number (or \(-1\) for fake hits) as input. The algorithm produces candidate tracks as output, which are then converted into the hit index format and compared with the real tracks based on complete coincidence. The Ariadne library was used to train and test the model [16]. The algorithm is designed to improve the reconstruction of track trajectories in particle collision experiments. One sub-task is finding parameters to fit a helical line to a set of points along the track trajectory, which allows for filtering out false tracks by a proposed  
Figure 13: Dependence of the recall and precision metrics on the value of the screening criterion. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **0 Noise Level** & **1 st Noise Level** & **2 nd Noise Level** \\  & **(0 False Hits)** & **(100 False Hits)** & **(1000 False Hits)** \\ \hline Mean value of the & & & \\ center cluster & 0.0005 & 0.0011 & 0.0071 \\ Standard deviation & & & \\ value of the center & 0.0012 & 0.0065 & 0.0142 \\ cluster & & & \\ Number of elements & 16,422 & 12,319 & 10,343 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Center cluster chi-squared depending on zero level of noise, 100 false hits, 1000 false hits. chi-squared error criterion. To speed up the track reconstruction process, we proposed a basic approach to parallelize the task of finding helix parameters for multiple tracks simultaneously. This approach significantly improves performance, as shown in the experimental results. The track reconstruction algorithm proposed in our paper consists of three major steps:  
1. The input data in the form of a DataFrame containing hit coordinates \((x,y,z)\), event and station identifiers, and the track number are fed into the neural network. 2. The network generates candidate tracks using the TrackNETv2.1 model. 3. A thresholding procedure is applied to cluster the track candidates based on the chi-squared values obtained from fitting a helix to each trajectory. The tracks are then labeled as either false or correct. The goal of the algorithm is to take in a DataFrame containing hit coordinates, event and station identifiers, and the track number and generate labeled tracks. The input DataFrame is denoted as \(D\) and the output labeled tracks are denoted as \(T\). The algorithm begins by loading the TrackNETv2.1 model, which will be used to generate candidate tracks. The function GenerateCandidates is then called to generate candidate tracks from the input DataFrame \(D\). This function loops over each hit in \(D\) and applies the TrackNETv2.1 model to generate a candidate track for each hit. The candidate tracks are stored in a list called \(C\). Once the candidate tracks are generated, the function LabelTracks is called to apply a thresholding procedure to cluster the track candidates based on the chi-squared values obtained from fitting a helix to each trajectory. This function loops over each candidate track in \(C\) and fits a helix to the track. The chi-squared value is then calculated for the fitted helix.