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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.26280 |
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| _version_ | 1866914602160226304 |
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| author | Soloveva, Olga Belousov, Artemiy Kisel, Ivan Bratkovskaya, Elena |
| author_facet | Soloveva, Olga Belousov, Artemiy Kisel, Ivan Bratkovskaya, Elena |
| contents | Modern high-rate experiments require rare physics signatures to be identified in real time from continuous streams of reconstructed events under stringent data-throughput and storage constraints. We present a convolutional-neural-network-based trigger concept for selecting events associated with quark-gluon plasma (QGP) formation. Events are encoded as compact multidimensional histograms of reconstructed particle content, including particle species, momentum magnitude, and angular information. The method is first evaluated within the Parton-Hadron-String Dynamics (PHSD) framework, where microscopic QGP-related labels are available. As an independent validation, the same event representation and network architecture are applied to Ultra-relativistic Quantum Molecular Dynamics (UrQMD) simulations, providing a distinct description of the collision dynamics. Cross-checks between PHSD and UrQMD are used to assess the stability of the learned response against generator-dependent effects and to quantify model-transfer robustness. For realistic deployment, a lightweight C++ inference package, ANN4FLES, is employed at the physics-analysis stage after tracking and topology reconstruction. For Au+Au collisions at 30 AGeV, the classification accuracy decreases from 95.1% on generator-level PHSD events to 83.7% after full reconstruction, while retaining practical separation power for online event selection. SHAP-based interpretability analysis is used to identify the dominant particle-species contributions to the network decision. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_26280 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CNN-Based Online Trigger for QGP Event Selection Soloveva, Olga Belousov, Artemiy Kisel, Ivan Bratkovskaya, Elena Nuclear Theory Modern high-rate experiments require rare physics signatures to be identified in real time from continuous streams of reconstructed events under stringent data-throughput and storage constraints. We present a convolutional-neural-network-based trigger concept for selecting events associated with quark-gluon plasma (QGP) formation. Events are encoded as compact multidimensional histograms of reconstructed particle content, including particle species, momentum magnitude, and angular information. The method is first evaluated within the Parton-Hadron-String Dynamics (PHSD) framework, where microscopic QGP-related labels are available. As an independent validation, the same event representation and network architecture are applied to Ultra-relativistic Quantum Molecular Dynamics (UrQMD) simulations, providing a distinct description of the collision dynamics. Cross-checks between PHSD and UrQMD are used to assess the stability of the learned response against generator-dependent effects and to quantify model-transfer robustness. For realistic deployment, a lightweight C++ inference package, ANN4FLES, is employed at the physics-analysis stage after tracking and topology reconstruction. For Au+Au collisions at 30 AGeV, the classification accuracy decreases from 95.1% on generator-level PHSD events to 83.7% after full reconstruction, while retaining practical separation power for online event selection. SHAP-based interpretability analysis is used to identify the dominant particle-species contributions to the network decision. |
| title | CNN-Based Online Trigger for QGP Event Selection |
| topic | Nuclear Theory |
| url | https://arxiv.org/abs/2605.26280 |