## 2 Materials and Methods
### Simulated Data and Deep Learning Model for \(\Lambda\) Particle Classification  
Since the CBM experiment is still under construction, applying the algorithms to real data is not yet feasible. However, well-established simulation frameworks for heavy-ion physics are widely accepted and utilized. One such tool is the Ultra-relativistic Quantum Molecular Dynamics (UrQMD) framework [29], which generates raw heavy-ion collision \begin{table}
\begin{tabular}{l l} \hline \hline
**Input Features** & **Description** \\ \hline \(m\) & Mass of the reconstructed \(\Lambda\) particle. \\ \hline \(p_{T}\) & Transverse momentum of the reconstructed \(\Lambda\) particle. \\ \hline \(\chi^{2}_{geo}\) & \(\chi^{2}\) geometry value of the reconstructed \(\Lambda\), used for constructing mother particle candidates. \\ \hline \(\chi^{2}_{prim}\) & \(\chi^{2}\) primary value of the reconstructed \(\Lambda\), used to separate primary and secondary particles/tracks. \\ \hline \(\chi^{2}_{topo}\) & \(\chi^{2}\) topology value of the reconstructed \(\Lambda\) that is used for evaluating the reconstructed particle’s topology. (\(\chi^{2}_{topo}=\chi^{2}_{geo}+\chi^{2}_{prim}\)) \\ \hline \(l/\Delta l\) & Decay length normalized by its error (uncertainty) of the reconstructed \(\Lambda\). \\ \hline \(p_{T_{tangbrt1}}\) & Transverse momentum of the positively charged daughter particle of the reconstructed \(\Lambda\). \\ \hline \(p_{T_{tangbrt2}}\) & Transverse momentum of the negatively charged daughter particle of the reconstructed \(\Lambda\). \\ \hline \(\chi^{2}_{prim_{tangbrt1}}\) & \(\chi^{2}_{prim}\) of the positively charged daughter particle of the reconstructed \(\Lambda\). \\ \hline \(\chi^{2}_{prim_{tangbrt2}}\) & \(\chi^{2}_{prim}\) of the negatively charged daughter particle of the reconstructed \(\Lambda\). \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Selected input features for the deep learning approach. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Default Cut** & **Relaved Cut** & **Cut Description** \\ \hline \(\chi^{2}_{prim}>18.42\) & \(\chi^{2}_{prim}>4.605\) & A particle’s track is considered a secondary track when \(\chi^{2}_{prim}\) is larger than \\  & the threshold. \\ \hline \(\chi^{2}_{geo}<3\) & \(\chi^{2}_{geo}<6\) & The \(\chi^{2}_{geo}\) criterion evaluates the quality of a possible intersection of two daughter \\  & particle candidates’ tracks. \\ \hline \(l/\Delta l>3\) & \(l/\Delta l>2\) & Ensures the candidate’s decay point is significantly distant from the primary vertex, \\  & normalized by its uncertainty. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Default cut thresholds of the KF Particle Finder for the CBM setup and the relaxed cut thresholds used in the presented deep learning approach. data across a wide range of energies. These simulated datasets offer the advantage of known ground truth, making them ideal for supervised learning approaches. The data used in this work originate from CBM repositories, which provide a collection of validated datasets specifically prepared for algorithm development and testing. The CBM event reconstruction pipeline utilizes the Monte Carlo (MC) information from the simulation by making comparisons between algorithmic results and MC information at each phase. For instance, in the KF Particle Finder package, one attempts to match reconstructed particles with their respective MC particles. A reconstructed particle is called a signal if it is reconstructed and matched with the respective particle of the same type from the MC data. Physical background refers to reconstructed particles that match an MC particle but of a different type. Thus, this background is usually produced by other types of particles that have a similar topology. Combinatorial background (or ghosts) are reconstructed particles that do not match any MC particle and, therefore, lack physical meaning.