  The impact parameter characterizes the centrality in nucleus-nucleus
collision geometry. The determination of impact parameters in real experiments
is usually based on the reconstructed tracks and hits, or the derived
event-level observables. For the scheduled Cooler-storage-ring External-target
Experiment (CEE), the low beam energy results in a reduction of dependency
between the impact parameter and charged particle multiplicity, which decreases
the validity of the explicit determination methods. This work implements a few
neural networks that directly take the digitized signals from the external
Time-of-flight detectors as input. The model with the best performance has
shown a mean absolute error of 0.496 fm with the simulated UU collisions at 0.5
AGeV. Performances of these models with digi inputs are compared with reference
models with phase space inputs, showing the capacity of neural networks in
handling the original but potentially interrelated digitized signal
information.

High Energy Physics - Experiment;Instrumentation and Detectors