## 4 Discussion  
The results of this work demonstrate a significant reduction in background while keeping most of the particles' signal, as shown in Figure 5. This is specifically true when comparing to the Monte Carlo information of the simulated data. However, the mass is typically used as the final observable in physics analyses. Including it--or variables strongly correlated with it--within the classifier may bias the mass spectrum, artificially sharpening the signal peak and suppressing the sidebands. Although the metrics indicate good performance of the neural network, this can compromise the reliability of background estimation and signal extraction, particularly when applying the model to experimental data and therefore requires thorough examination.  
To investigate this effect, we conducted tests using the training and validation datasets, excluding the mass and other kinematically correlated variables (specifically, the transverse momenta of \(\Lambda\) and its daughters). The classifier still achieved strong results. When only the \(\Lambda\) mass was removed, the model reached an accuracy of 0.9745, with a precision and F1-score of 0.9746 and 0.9745, respectively. When both the mass and all transverse momenta were excluded--leaving only cut-based features such as primary vertex and track quality variables--the model still achieved an accuracy of 0.9444, with precision and F1-score exceeding 0.94. These results confirm that a comparable signal-background discrimination is achievable even without relying on mass-correlated features, thus preserving the integrity of the mass as an unbiased observable in subsequent physics analyses using experimental data.  
It should be emphasized that the results presented in this work were obtained using simulated central collisions, which represent idealized \"perfect\" scenarios. Future investigations should evaluate the performance of the proposed approach in simulated minimum bias collisions, where less ideal interactions and a wider variety of collision geometries are present. This will provide a more realistic assessment of the method's robustness and its applicability to a broader range of collision conditions--a crucial step toward its use with experimental data. Furthermore, the presented deep learning approach was only trained, validated, and tested on simulated data. While these simulations are typically well studied and assumed to reflect real data reasonably well, the method must be thoroughly studied before being applied to experimental data.  
The strong suppression of combinatorial background highlights the potential of machine learning in this domain. However, physical background arises from real particles mistakenly identified as \(\Lambda\) particles. Completely removing such a background negatively impacts the quality of the physics analysis for other particles. To address this, two extensions of the approach are proposed. Firstly, the analysis should include other particles of interest, especially those that typically interact or overlap with \(\Lambda\) particles, contributing to the mutual physical background. Secondly, the deep learning approach could be expanded to classify not only signal and background for \(\Lambda\) but also for multiple particle  
\begin{table}
\begin{tabular}{l r r} \hline  & **S/B** & \(\mathbf{S/\sqrt{S+B}}\) \\ \hline Default & 3.49 & 11.38 \\ \hline Neural Network & 38.28 & 12.95 \\ \hline Improvement Factor & 10.97 & 1.14 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of the S/B ratio and significance \(\left(\mathbf{S/\sqrt{S+B}}\right)\) of the default approach and the deep-learning-based approach.  
species. An extended neural network could potentially distinguish physical background for one particle as a valid signal for another, ensuring a more comprehensive and balanced reconstruction process. With such an approach, the physical background might be reduced without compromising the reconstruction quality of other particles while effectively eliminating combinatorial background with no physical significance.