## 3 Results  
For the results presented in this work, UrQMD-simulated gold-gold central collisions at 10 GeV were utilized. The training and validation datasets were artificially balanced to contain 50% signal and 50% background, ensuring even representation for training. A total of 787,000 \(\Lambda\) particles were used, with an 80:20 split for training and validation, respectively. Figure 1: Illustration of the deep learning model used for the presented results. The training was performed over 30 epochs with a batch size of 100 samples. The deep learning model achieved a classification accuracy of 98.76% on the validation set, which was used to select a well-performing model. The progression of the training process over 30 epochs is shown in Figure 2.  
Following the training phase, the pre-trained model was integrated into the KF Particle Finder to classify \(\Lambda\) particles within the CBM reconstruction pipeline. This classification was performed using 46,000 new collisions. The subsequent results presented here are directly derived from the KF Particle Finder package 22.2.99. In Figure 3, the mass distribution in the range from 1.06 \(\mathrm{GeV}/\mathrm{c}^{2}\) to 1.2 \(\mathrm{GeV}/\mathrm{c}^{2}\) for all reconstructed \(\Lambda\) particles is shown, consisting of signal, physical background, and ghosts. The actual \(\Lambda\) particles are concentrated around the mass region of 1.115 \(\mathrm{GeV}/\mathrm{c}^{2}\), corresponding to the expected \(\Lambda\) mass. While background particles may also appear near the \(\Lambda\) mass region, the majority are located in the surrounding areas. As shown, the overall number of reconstructed \(\Lambda\) particles is reduced by the deep learning approach (red) compared to the default method (blue), with the reduction primarily occurring in regions dominated by background. Figure 3: Comparison of all created \(\Lambda\) particles (signal and background) of the default approach (blue) and deep learning approach (red) in the mass range from 1.06 \(\mathrm{GeV}/\mathrm{c}^{2}\) to 1.2 \(\mathrm{GeV}/\mathrm{c}^{2}\). Figure 2: Accuracy over 30 epochs of training and validation with a peak performance of 98.76% on the validation set. True positives are those \(\Lambda\) particles that are correctly reconstructed and identified, also known as signal. The histogram of the true positives of the default approach and the deep learning approach can be seen in Figure 4a. In the mass range of 1.1 GeV/c\({}^{2}\) to 1.135 GeV/c\({}^{2}\), the deep learning approach has reconstructed 179,891 true positives, 1.36% fewer true \(\Lambda\) particles than the default approach, which reconstructs 182,364 true \(\Lambda\) particles. Thus, slightly more \(\Lambda\) particles were incorrectly classified as background in comparison to the default approach based on pre-defined cuts. The total background, here false positives, can be seen in Figure 4b. With 25,761 incorrectly classified \(\Lambda\) particles, the deep learning approach produces 95.94% less total background than the default approach, which incorrectly classified 633,771 particles as \(\Lambda\). Since there are two types of background with different meanings for physics analysis, it is important to analyze both separately. In Figure 4c, the results of the physical background can be seen. Both approaches encountered difficulties in the removal of the background in the anticipated mass region of the \(\Lambda\) particle. Nevertheless, the deep learning approach exhibited superior performance in the reduction of background in comparison to the standard method. In the remaining regions, the reductionof background is more evident. Here, the deep learning approach demonstrated a markedly superior reduction compared to the default approach. Overall, within the range of 1.06 GeV/c\({}^{2}\) to 1.2 GeV/c\({}^{2}\), the standard approach misclassified 34,746 of the observed particles, while the deep learning approach misclassified only 8314.