https://doi.org/10.1051/epjconf/202429502018
Level-3 Trigger for CLAS12 with Artificial Intelligence
1 SUPA, School of Physics and Astronomy, University of Glasgow, Glasgow G12 8QQ, United Kingdom
2 Jefferson Lab, Newport News, VA, USA
* e-mail: tyson@jlab.org
Published online: 6 May 2024
Fast, efficient and accurate triggers are a critical requirement for modern high energy physics experiments given the increasingly large quantities of data that they produce. The CEBAF Large Acceptance Spectrometer (CLAS12) employs a highly efficient electron trigger to filter the amount of data recorded by requiring at least one electron candidate in each event, at the cost of a low purity in electron identification. However, machine learning algorithms are increasingly employed for classification tasks such as particle identification due to their high accuracy and fast processing times. In this proceeding we present recently published work that showed how a convolutional neural network could be deployed as a Level 3 electron trigger at CLAS12. We demonstrate that this AI trigger would achieve a significant data reduction compared to the conventional CLAS12 electron trigger, whilst preserving a 99.5% electron identification efficiency, at nominal CLAS12 beam currents.
© 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.
