  In this article, we use artificial intelligence algorithms to show how to
enhance the resolution of the elementary particle track fitting in
inhomogeneous dense detectors, such as plastic scintillators. We use deep
learning to replace more traditional Bayesian filtering methods, drastically
improving the reconstruction of the interacting particle kinematics. We show
that a specific form of neural network, inherited from the field of natural
language processing, is very close to the concept of a Bayesian filter that
adopts a hyper-informative prior. Such a paradigm change can influence the
design of future particle physics experiments and their data exploitation.
