Document Type

Conference Paper

Publication Date

2024

DOI

10.1051/epjconf/202429509038

Publication Title

EPJ Web of Conferences

Volume

295

Pages

09038 (1-9)

Conference Name

26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023), 8-12 May 2023, Norfolk, VA

Abstract

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.

Rights

© 2024 The Authors.

This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Original Publication Citation

Gavalian, G., Thomadakis, P., Angelopoulos, A., & Chrisochoides, N. (2024). Charged track reconstruction with artificial intelligence for CLAS12. EPJ Web of Conferences, 295, 09038. https://doi.org/10.1051/epjconf/202429509038

ORCID

0000-0002-4299-570X (Thomadakis), 0000-0002-6498-5633 (Angelopoulos), 0000-0003-3088-0187 (Chrisochoides)

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