Application of Neural Networks for Event-by-Event Evaluation of the Impact Parameter

Evaluation of the impact parameter in single events is crucial for the correct and efficient data processing in collision-based nuclear and particle physics experiments. Estimating the impact parameter in real time allows experimentalists to preselect the most informative events at the data acquisition stage before any processing. The presented computational experiments show the applicability of neural networks combined with the microchannel plate detectors to directly estimate the impact parameter at the experimental facilities under construction.