Deep Learning for Longitudinal Asymmetry in Heavy-Ion Collisions at RHIC Energies
We developed a deep neural network framework to predict the longitudinal asymmetry and the corresponding rapidity shift in relativistic heavy-ion collisions. The model was trained using sufficiently large datasets for Au+Au and Cu+Cu collision systems at center-of-mass energies of 62.4 and 200 AGeV at RHIC. It successfully learns the underlying relationships between the input parameters and reconstructs the corresponding target quantities with good precision. Its predictions agree well with theoretical simulations across both trained and untrained centrality intervals. The influence of longitudinal asymmetry on final-state flow observables has also been studied, and the model results are in reasonable agreement with the simulation. A SHAP-based correlation analysis further supports the physical consistency of the learned feature dependencies and suggests that the observed correlations are not primarily driven by feature redundancy. The proposed framework provides a computationally efficient approach to estimating event-by-event results, potentially reducing the computational cost of large-scale theoretical model simulations. This work presents an initial study exploring the use of deep neural networks to investigate longitudinal asymmetry and rapidity shifts in relativistic heavy-ion collisions.