Particle Identification with Deep Neural Networks Across Collision Energies in Simulated Proton-Proton Collisions
Fuente:
arXiv
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866911053488586752 |
|---|---|
| author | Khalaf, Omar M. Hamed, Ahmed M. |
| author_facet | Khalaf, Omar M. Hamed, Ahmed M. |
| contents | This study demonstrates a proof-of-concept application of a deep neural network for particle identification in simulated high transverse momentum proton-proton collisions, with a focus on evaluating model performance under controlled conditions. A model trained on simulated Large Hadron Collider (LHC) proton-proton collisions at $\sqrt{s} = 13\,\mathrm{TeV}$ is used to classify nine particle species based on seven kinematic-level features. The model is then tested on simulated high transverse momentum Relativistic Heavy Ion Collider (RHIC) data at $\sqrt{s} = 200\,\mathrm{GeV}$ without any transfer learning, fine-tuning, or weight adjustment. It maintains accuracy above 91% for both LHC and RHIC sets, while achieving above 96% accuracy for all RHIC sets, including the $p_T > 7\,\mathrm{GeV}/c$ set, despite never being trained on any RHIC data. Analysis of per-class accuracy reveals how quantum chromodynamics (QCD) effects, such as leading particle effect and kinematic overlap at high $p_T$, shape the model's performance across particle types. These results suggest that the model captures physically meaningful features of high-energy collisions, rather than simply overfitting to kinematics of the training data. This study demonstrates the potential of simulation-trained deep neural networks to remain effective across lower energy regimes within a controlled environment, and motivates further investigation in realistic settings using detector-level features and more advanced network architectures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06732 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Particle Identification with Deep Neural Networks Across Collision Energies in Simulated Proton-Proton Collisions Khalaf, Omar M. Hamed, Ahmed M. High Energy Physics - Experiment This study demonstrates a proof-of-concept application of a deep neural network for particle identification in simulated high transverse momentum proton-proton collisions, with a focus on evaluating model performance under controlled conditions. A model trained on simulated Large Hadron Collider (LHC) proton-proton collisions at $\sqrt{s} = 13\,\mathrm{TeV}$ is used to classify nine particle species based on seven kinematic-level features. The model is then tested on simulated high transverse momentum Relativistic Heavy Ion Collider (RHIC) data at $\sqrt{s} = 200\,\mathrm{GeV}$ without any transfer learning, fine-tuning, or weight adjustment. It maintains accuracy above 91% for both LHC and RHIC sets, while achieving above 96% accuracy for all RHIC sets, including the $p_T > 7\,\mathrm{GeV}/c$ set, despite never being trained on any RHIC data. Analysis of per-class accuracy reveals how quantum chromodynamics (QCD) effects, such as leading particle effect and kinematic overlap at high $p_T$, shape the model's performance across particle types. These results suggest that the model captures physically meaningful features of high-energy collisions, rather than simply overfitting to kinematics of the training data. This study demonstrates the potential of simulation-trained deep neural networks to remain effective across lower energy regimes within a controlled environment, and motivates further investigation in realistic settings using detector-level features and more advanced network architectures. |
| title | Particle Identification with Deep Neural Networks Across Collision Energies in Simulated Proton-Proton Collisions |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2505.06732 |