Artificial intelligence for improved fitting of trajectories of elementary particles in inhomogeneous dense materials immersed in a magnetic field
This work addresses the challenge of particle track fitting in dense detectors for particle physics experiments, potentially influencing future experimental design and data analysis.
The paper tackled the problem of fitting elementary particle trajectories in inhomogeneous dense materials within magnetic fields by using deep learning to replace traditional Bayesian filtering methods, resulting in drastically improved reconstruction of particle kinematics.
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.