https://doi.org/10.1051/epjconf/202429509038
Charged Track Reconstruction with Artificial Intelligence for CLAS12
1 Jefferson Lab, Newport News, VA, USA
2 CRTC, Department of Computer Science, Old Dominion University, Norfolk, VA, USA
* e-mail: gavalian@jlab.org
** e-mail: pthom001@odu.edu
*** e-mail: aangelos28@gmail.com
**** e-mail: npchris@gmail.com
Published online: 6 May 2024
In this paper, we present the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use neural networks working together to identify tracks based on the raw signals in the Drift Chambers. A Convolutional Auto-Encoder is used to de-noise raw data by removing the hits that do not satisfy the patterns for tracks, and second Multi-Layer Perceptron is used to identify tracks from combinations of clusters in the drift chambers. Our method increases the tracking efficiency by 50% for multi-particle final states already conducted experiments. The de-noising results indicate that future experiments can run at higher luminosity without degradation of the data quality. This in turn will lead to significant benefits for the CLAS12 physics program.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
