Machine Learning-Based Classification of Synthetic Quantum Entanglement–Like Events in High-Energy Collisions Using TMVA

Quantum entanglement is often discussed as a key quantum feature that may influence the dynamics of high-energy particle collisions. However, identifying such effects directly in experimental measurements is still extremely challenging because the signals are subtle and easily masked by background processes. In this work, we construct a simplified event generator in ROOT to explore whether these signatures can be distinguished in a controlled environment. The generator produces two kinds of events: one in which particles are emitted with random azimuthal angles, and another in which selected particle pairs are generated with intentionally small azimuthal separations, mimicking entanglement-like correlations. From each event we extract several global observables such as charged-particle multiplicity, the mean azimuthal-angle separation, and transverse spherocity which are sensitive to changes in event topology. Using these variables, we train a Boosted Decision Tree (BDT) within the TMVA framework on a balanced sample of 10,000 events. The performance of the trained model shows that machine-learning techniques are capable of identifying the weak correlation patterns embedded in the synthetic data, suggesting that similar strategies may help in future searches for quantum-correlation effects in real collision systems.Based on this framework, advanced classification methods can be applied to real collision data, which potentially enhancing searched for entanglement related signatures in high-energy physics.