Artificial intelligence for improved fitting of trajectories of elementary particles in dense materials immersed in a magnetic field

Particle track fitting is crucial for understanding particle kinematics. In this article, we use artificial intelligence algorithms to show how to enhance the resolution of the elementary particle track fitting in 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. Particle track fitting in dense detectors is crucial for understanding particle kinematics. The authors show how deep learning outperforms traditional Bayesian filtering methods, drastically improving the reconstruction of interacting particles and potentially impacting the design and data exploitation of future particle physics experiments.