Speaker
Gavalian, Gagik
(Jefferson Lab)
Description
This work shows 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 a 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 $35\%$.
| Consider for long presentation | Yes |
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Primary author
Gavalian, Gagik
(Jefferson Lab)
