## 2 Materials and Methods
### Data Extraction Using the KF Particle Finder Package  
In CBM, the performance of algorithms within the FLES package is measured by a comparison of Monte Carlo simulated data and reconstructed results by the algorithm packages. Using this approach, the reconstruction efficiency and precision of each part of the package can be evaluated. The Monte Carlo data can be considered as the true outcomes of each event, allowing the application of supervised learning techniques. In the stage of the KF Particle Finder, when the particles and decay chains are reconstructed, they are matched with the corresponding Monte Carlo true particle. In this work, the Monte Carlo information of each matched particle is utilized to check if the reconstructed particle is a true \(K_{\rm s}\)-meson or \(\Lambda\)-hyperon. If this is the case, the information of the corresponding reconstructed particle is extracted to build a dataset. In real experiments, Monte Carlo information does not exist and, therefore, the neural network has to perform on reconstructed information and hypothesis only.  
Overall, 25,000 events generated by the UrQMD model [21] were used in this work. A total of 12,000 events were used for training and testing, whereas 13,000 were used to evaluate the network's performance in comparison to the existing approach. All events are central Au+Au collisions at 10 GeV energy, and therefore within the specifications of the CBM experiment. In general, the created generated particles are processed by a transport engine (e.g., GEANT4 [22]) to simulate the particles flying through the detector system, including all relevant physics processes such as decays, scattering and interaction [23]. At this point, the whole event outcome is simulated, including all decay chains and trajectories. Afterward, the detector responses are generated, resulting in measurements (hits) that would also be produced by real experiment particles when they interact with a detector. The hits are then used to reconstruct tracks and the KF Particle Finder is applied to reconstruct short-lived particles and decays.  
The first part of the dataset (12,000 events) is used to train and test the neural network architectures in the Artificial Neural Networks for First Level Event Selection (ANN4 FLES) [24] standalone package. Here, a measurement for raw classification performance was obtained by the accuracy and cross entropy loss values per epoch. This dataset was divided by a 80:20 ratio for a training dataset and a testing dataset, respectively. Several fully-connected neural network architectures and settings were tested by hand, where a multi-layer perceptron with three hidden layers with a peak accuracy of more than 98% on the test set was finally chosen.  
The second part of that data set, consisting of 13,000 events, is used for comparison of the neural network based approach and the existing particle competition within the KF Particle Finder. Hence, after training and testing, the pre-trained neural networks are evaluated, implemented in the KF Particle Finder package as a part of the reconstruction chain of the CBM experiment. That way, it is possible to use the tools for performance measurement included in the KF Particle Finder, that are a well-established standard to evaluate the algorithms' performance in heavy-ion physics experiments.