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A deep learning based method with the convolutional neural network (CNN)algorithm for determining the impact parameters is developed using theconstrained molecular dynamics model simulations, focusing on the heavy-ioncollisions at the low-intermediate incident energies from several ten to onehundred MeV/nucleon in which the emissions of heavy fragments with the chargenumbers larger than 3 become crucial. To make the CNN applicable in the task ofthe impact parameter determination at the present energy range, specificimprovements are made in the input selection, the CNN construction and the CNNtraining. It is demonstrated from the comparisons of the deep CNN method andthe conventional methods with the impact parameter-sensitive observables, thatthe deep CNN method shows better performance for determining the impactparameters, especially leading to the capability of providing betterrecognition of the central collision events. With a proper consideration of theexperimental filter effect in both training and testing processes to keepconsistency with the actual experiments, the good performance of the deep CNNmethod holds, and shows significantly better in terms of predicting the impactparameters and recognizing the central collision events, compared to that ofthe conventional methods, demonstrating the superiority of the present deep CNNmethod. The deep CNN method with the consideration of the filter effect isapplied in the deduction of nuclear stopping power. Higher accuracy for thestopping power deduction is achieved benefitting from the better impactparameter determination using the deep CNN method, compared to using the theconventional methods. This result reveals the importance to select a reliableimpact parameter determination method in the experimental deduction of thenuclear stopping power as well as other observables.

Determining impact parameters of heavy-ion collisions at low-intermediate incident energies using deep learning with convolutional neural network