Publications
Publication Information
| Title | CLAS12 Track Reconstruction with Artificial Intelligence |
| Authors | Gagik Gavalian, Polykarpos Thomadakis, Angelos Angelopoulos, Raffaella Vita, Veronique Ziegler, Nikos Chrisochoides |
| JLAB number | JLAB-PHY-22-3566 |
| LANL number | arXiv:2202.06869 |
| Other number | DOE/OR/23177-5433 |
| Document Type(s) | (Journal Article) |
| Associated with EIC: | No |
| Supported by Jefferson Lab LDRD Funding: | No |
| Funding Source: | Advanced Scientific Computing Research (ASCR) |
|
Journal Compiled for Physical Review X | |
| Publication Abstract: | 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 $35\%$. |
| Experiment Numbers: | E12-06-119 |
| Group: | Hall B |
| Document: | |
| DOI: | |
| Accepted Manuscript: | |
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