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Publication Information
Title Deep Learning Level-3 Electron Trigger for CLAS12
Abstract 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 recorded data by requiring at least one electron in each event, at the cost of a low purity in electron identification. Machine learning algorithms are increasingly employed for classification tasks such as particle identification due to their high accuracy and fast processing times. In this article, we show how a convolutional neural network could be deployed as a Level 3 electron trigger at CLAS12. We demonstrate that the AI trigger would achieve a significant data reduction compared to the traditional trigger, whilst preserving a 99.5\% electron identification efficiency. The AI trigger purity as a function of increased luminosity is improved relative to the traditional trigger. As a consequence,
Author(s) Richard Tyson, Gagik Gavalian, David Ireland, Bryan McKinnon
Publication Date September 2023
Document Type Journal Article
Primary Institution Thomas Jefferson National Accelerator Facility, Newport News
Affiliation Exp Nuclear Physics / Experimental Halls / Hall B
Funding Source Nuclear Physics (NP)
Proprietary? No
This publication conveys Technical Science Results
Document Numbers
JLAB Number: JLAB-PHY-23-3759 OSTI Number: 1997948
LANL Number: arXiv:2302.07635 Other Number: DOE/OR/23177-5741
Associated with an experiment No
Associated with EIC No
Supported by Jefferson Lab LDRD Funding No
Journal Article
Journal Name Computer Physics Communication
Refereed No
Volume 290
Issue
Page(s) 108783
Attachments/Datasets/DOI Link
Document(s)
2302.07635.pdf (STI Document)
1-s2.0-S0010465523001285-main.pdf (Accepted Manuscript)
DOI Link
Dataset(s) (none)
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