## 3 Results  
### Particle Competition Based on Mass and PDG Mass  
The MLP implemented using ANN4 FLES provides a raw classification accuracy of up to 98.6% for the testing set (see Figure 2). An almost identically constructed neural network was implemented in PyTorch to ensure valid results. The PyTorch architecture provided equally high accuracy values and again confirmed the classification performance of ANN4 FLES. An error of only 1.4% on the testing set suggests that the classification performance of the more complex ANN4 FLES architecture is more suitable to solve the task, since the error rate of the previous work is given with more than 10%. There are several possible  
Figure 1: Multi-Layer Perceptron (MLP) topology used to classify \(K_{s}\) and \(\Lambda\), using 3 hidden layers with 8 neurons each, hidden activation function Leaky-ReLU. Output layer consists of two neurons with Softmax activation and cross entropy loss. Figure 2: ANN4 FLES and PyTorch accuracy for training and testing, using reconstructed mass and PDG mass over 100 epochs with a peak performance of 98.6% in the testing set. Both networks, ANN4 FLES and PyTorch, achieved high accuracy values on the testing set. reasons for the better results, such as the topology, different learning rate or the loss minimizing algorithm, which was Broyden-Fletcher-Goldfarb-Shanno (BFGS) [15; 31; 32] in the previous work. Furthermore, although the generators for simulated events should perform equally, further investigations are suggested, as previous work used the PHSD [33] model to generate data for the neural network training, testing and evaluation, whereas in the present work, UrQMD is the underlying model to generate event data. In Figure 3, the total mass spectra histograms for \(K_{s}\) and \(\Lambda\) are shown. The results using KF Particle Finder without competition (black) and with the existing method (green) are visualized for comparison. The ANN4 FLES approach is colored red, and one can see that it seems to reduce the number of entries over the whole range. In areas with larger distance to the peak, this is likely to be reduced background. Nevertheless, there is also a reduction in entries at the peak area for \(K_{s}\), where a signal rejection can be the reason. In general, it reduces the amount of particles that were finally classified as \(K_{s}\) or \(\Lambda\), respectively. Investigating the signal mass histograms in Figure 4 shows that, in both cases, a slight reduction in signal is indicated compared to running the KF Particle Finder without competition. For \(K_{s}\), the ANN4 FLES approach rejected slightly more signal compared to the existing method, whereas for \(\Lambda\), the results are the other way around. Here, the existing method rejects more signal particles than ANN4 FLES. Thus, one can assume that the largest part of rejected particles in Figure 3 is due to correctly rejected background. In Figure 5, the background mass distribution is shown. Based on the histogram for \(\Lambda\), it is difficult to see which competition approach is better. Both competitions reduce the background significantly but, around the peak, the existing method seems to perform better, since there is a clearly visible peak for ANN4 FLES at \(\Lambda\)'s PDG mass \(1.116\) GeV/\(c^{2}\), whereas in the whole range, ANN4 FLES seems to reduce the background slightly more. Moreover, there is a large peak of the existing method at \(m<1.11\) GeV/\(c^{2}\), indicating less rejected background by the existing method compared to the neural-network-based approach. For \(\Lambda\), this histogram indicates a tie between ANN4 FLES and the existing method. However,  
Figure 4: Histograms of signals for \(K_{s}\) (**left**) and \(\Lambda\) (**right**) masses. Comparison of no competition (black), the existing mother particle competition (green) and ANN4 FLES (red). Figure 3: Histograms of mass spectra for \(K_{s}\) (**left**) and \(\Lambda\) (**right**).