Determining impact parameters of heavy-ion collisions at low-intermediate incident energies using deep learning with convolutional neural networks
| Pub type: | Article |
| Citation: | PhysRevC.105.034611 |
| Status: | Published |
| Journal: | Phys. Rev. C |
| Volume: | 105 |
| Year: | 2022 |
| Month: | Mar |
| Pages: | 034611 |
| Publisher: | American Physical Society |
| URL: | https://link.aps.org/doi/10.11... |
| DOI: | 10.1103/physrevc.105.034611 |
| Abstract: | A deep learning based method with the convolutional neural network (CNN) algorithm for determining the impact parameters is developed by using constrained molecular-dynamics model simulations, focusing on the heavy-ion collisions at the low-intermediate incident energies from several tens to one hundred MeV/nucleon in which the emissions of heavy fragments with the charge numbers larger than three become crucial. To make the CNN applicable in the task of the impact-parameter determination at the present energy range, specific improvements are made in the input selection, the CNN construction, and the CNN training. It is demonstrated from the comparisons of the deep CNN method and the conventional methods with the impactparameter- sensitive observables that the deep CNN method shows better performance for determining the impact parameters, especially leading to the capability of providing better recognition of the central collision events. With a proper consideration of the experimental filter effect in both training and testing processes to keep consistency with the actual experiments, the good performance of the deep CNN method holds and performs significantly better in terms of predicting the impact parameters and recognizing the central collision events compared with conventional methods, demonstrating the superiority of the present deep CNN method. The deep CNN method with the consideration of the filter effect is applied in the deduction of nuclear stopping power. Higher accuracy for the stopping power deduction is achieved benefitting from the better impact-parameter determination using the deep CNN method, compared with using conventional methods. This result reveals the importance of selecting a reliable impact-parameter-determination method in the experimental deduction of the nuclear stopping power as well as other observables. |
| Month_val: | 3 |
| Sig contrib: | X. Liu, H. Zheng, Roy Wada, A. Bonasera |
| Authors: | |
| Added by: | [DGM] |
| Total mark: | 0 |
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