## 3 Results
### Particle Competition Based on Mass and PDG Mass
Comparison of no competition (black), the existing mother particle competition (green) and the competition by ANN4 FLES (red). evaluating \(K_{s}\), there is no peak at the PDG mass of 0.493 GeV/\(c^{2}\) by ANN4 FLES. The neural network used seems to perform well in rejecting \(K_{s}\) background around the known mass peak, which, overall, increases the physics analysis for \(K_{s}\). Furthermore, a competition based on the mass is quite difficult, if the reconstructed mass of the background producing particle is within a range where it is expected for the respective investigated particle. These results show that the network is not only classifying by the distance to the mass distribution peak, as is performed in the existing method. In the ghost histograms (see Figure 6), ANN4 FLES seems to be ahead in background rejection in both cases \(\Lambda\) and \(K_{s}\). Although, similar to the existing method, ANN4 FLES has a peak around the PDG mass bins, the existing method has more ghosts over the whole range and within the peak area. Thus, the neural network rejects more ghost particles than the default approach and, therefore, helps to reduce the mistakenly created mother particle candidates even more. In general, the presented plots indicate a better performance of ANN4 FLES by rejecting background particles. This can be confirmed by the S/B ratio and significance. In Figure 7, the invariant mass spectra of \(K_{s}=\pi^{+}\pi^{-}\) and \(\Lambda=\mathrm{p}\pi^{-}\) for the existing competition are shown. With a significance of 149 and 213 for \(K_{s}\) and \(\Lambda\), respectively, the clear signal is given in both cases using the existing competition of the KF Particle Finder. Additionally, the S/B ratio of 3.58 shows that the data visualized in the \(K_{s}\)-plot consists of almost four times more signal than background, where it is over eight times more signal than background for \(\Lambda\). Figure 5: Histograms of background for \(K_{s}\) (**left**) and \(\Lambda\) (**right**) masses. Comparison of no competition (black), the existing mother particle competition (green) and ANN4 FLES (red). Figure 6: Histograms of ghosts for \(K_{s}\) (**left**) and \(\Lambda\) (**right**) masses. Comparison of no competition (black), the existing mother particle competition (green) and ANN4 FLES (red). The following results are achieved by the ANN4 FLES approach (Figure 8). Comparing the S/B ratio for \(K_{\rm s}\), one can see that the ANN approach improved the value by around 16%. For \(\Lambda\), however, the S/B ratio has been reduced by about 2%. Considering both particles, ANN4 FLES has reduced the background successfully even further. However, for \(K_{\rm s}\), the significance was also decreased by about 3%, whereas the significance of \(\Lambda\) was increased by 1%. That indicates that even if the background was reduced successfully on average, the fluctuation in the background has been increased on average in comparison to the existing method. Nevertheless, both significance values are high enough to consider these particles as a clear signal, making the minor reduction negligible.