2022/02/14 by Gagik Gavalian, Gavalian, Gagik, Polykarpos Thomadakis +9
Computer Science · Physics and Astronomy · #Advanced Data Storage Technologies #Data Analysis #FOS: Physical sciences #Nuclear Experiment (nucl-ex) #Particle Detector Development and Performance #Particle physics theoretical and experimental studies #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2202.06869
openalex publication_date 2022/02/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this article we describe the implementation of Artificial Intelligence models in track reconstruction software for the CLAS12 detector at Jefferson Lab. The Artificial Intelligence based approach resulted in improved track reconstruction efficiency in high luminosity experimental conditions. The track reconstruction efficiency increased by 10-12% for single particle, and statistics in multi-particle physics reactions increased by 15%-35% depending on the number of particles in the reaction. The implementation of artificial intelligence in the workflow also resulted in a speedup of the tracking by 35%.