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

TL;DR

This paper demonstrates how deep learning algorithms can significantly improve the accuracy of particle trajectory reconstruction in inhomogeneous dense detectors, potentially transforming data analysis in particle physics experiments.

Contribution

It introduces a novel application of neural networks, inspired by natural language processing, to replace traditional Bayesian filtering in particle track fitting.

Findings

Deep learning enhances particle trajectory resolution.

Neural networks outperform Bayesian filtering methods.

Potential impact on future particle physics experiments.

Abstract

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.