A decade of trends and progress in methods and hardware accelerators for particle track reconstruction: a systematic review

Abstract

Particle track reconstruction is crucial in high-energy physics experiments because it involves identifying the trajectories of charged particles produced during collisions. Precise reconstruction of particle tracks is necessary to determine the types of particles produced, measure their energies and momenta, and eventually comprehend the underlying physics processes. Over the years, various methods, approaches, and algorithms have been developed for particle track reconstruction; however, their effectiveness and limitations remain a subject of ongoing research. In this study, we aimed to synthesize the current knowledge on particle track reconstruction, identify areas of consensus, and look for directions for future research by using a systematic review. This review used three primary journal databases: Web of Science, Scopus, and ScienceDirect. As a result of these search efforts, 77 articles were identified. This systematic review presents a complete overview of the existing literature on particle track reconstruction from 2014 to 2023, with a focus on the methods and hardware accelerators used in high-energy physics experiments. We categorized the methods into three categories, including machine learning, and discussed the hardware accelerators, like the Graphical Processing Unit (GPU) and field-programmable gate array (FPGA).