Particle Identification with Deep Neural Networks Across Collision Energies in Simulated Proton-Proton Collisions

Abstract

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.

Revised version resubmitted to Journal of Physics G. Includes clarification of event generation settings, an extended comparison between LHC and RHIC kaons, and discussion of sources of uncertainty