STI Publications - View Publication Form #17627
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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 |
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| 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)
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| DOI Link | |
| Dataset(s) | (none) |
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