  A new method of event characterization based on Deep Learning is presented.
The PointNet models can be used for fast, online event-by-event impact
parameter determination at the CBM experiment. For this study, UrQMD and the
CBM detector simulation are used to generate Au+Au collision events at 10 AGeV
which are then used to train and evaluate PointNet based architectures. The
models can be trained on features like the hit position of particles in the CBM
detector planes, tracks reconstructed from the hits or combinations thereof.
The Deep Learning models reconstruct impact parameters from 2-14 fm with a mean
error varying from -0.33 to 0.22 fm. For impact parameters in the range of 5-14
fm, a model which uses the combination of hit and track information of
particles has a relative precision of 4-9 % and a mean error of -0.33 to 0.13
fm. In the same range of impact parameters, a model with only track information
has a relative precision of 4-10 % and a mean error of -0.18 to 0.22 fm. This
new method of event-classification is shown to be more accurate and less model
dependent than conventional methods and can utilize the performance boost of
modern GPU processor units.
