Speaker
Description
High-rate heavy-ion experiments require fast and robust event-selection strategies capable of identifying rare physics signatures in real time under severe throughput and storage constraints. We present a convolutional-neural-network-based trigger concept for the selection of events associated with quark–gluon plasma (QGP) formation. The method represents each collision event as a compact multidimensional histogram of reconstructed particle content, encoding particle species, momentum magnitude, polar angle, and azimuthal angle. This representation is processed by a lightweight 3D CNN architecture designed for fast inference in online or quasi-online analysis environments.
The classifier is trained and validated using events generated with the Parton–Hadron–String Dynamics (PHSD) transport approach, where microscopic QGP-related event labels are available. To assess model dependence, the same event representation and network architecture are further tested with UrQMD-based simulations, including cross-model validation between PHSD and UrQMD. This allows us to investigate whether the network learns robust QGP-sensitive final-state structures rather than generator-specific correlations. In addition, SHAP-based interpretability analysis is used to identify the particle species and phase-space regions most relevant for the network decision, with strange hadrons and antibaryon-related features providing particularly important contributions.
For realistic deployment, the method is evaluated along the transition from idealized generator-level information to reconstructed events in the CBM/FLES analysis chain. Using ANN4FLES for lightweight C++ inference, the classification accuracy for Au+Au collisions at 30 AGeV decreases from 95.1% at PHSD generator level to 83.7% after full reconstruction, indicating that substantial QGP-sensitive information survives detector acceptance, tracking, and topology reconstruction effects. These results demonstrate the potential of model-robust AI triggers for fast event enrichment in future high-rate heavy-ion experiments.