Publications
Publication Information
| Title | Deep Learning Level-3 Electron Trigger for CLAS12 |
| Authors | Richard Tyson, Gagik Gavalian, David Ireland, Bryan McKinnon |
| JLAB number | JLAB-PHY-23-3759 |
| LANL number | arXiv:2302.07635 |
| Other number | DOE/OR/23177-5741 |
| Document Type(s) | (Journal Article) |
| Associated with EIC: | No |
| Supported by Jefferson Lab LDRD Funding: | No |
| Funding Source: | Nuclear Physics (NP) |
|
Journal Compiled for Computer Physics Communication Volume 290 Page(s) 108783 | |
| Publication 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, this AI trigger can achieve a data recording reduction improvement of 0.33\% per nA when compared to the traditional trigger whilst maintaining an efficiency above 99.5\%. A reduction in data output also reduces storage costs and post-processing times, which in turn reduces the time to the publication of new physics measurements. |
| Experiment Numbers: | |
| Group: | Hall B |
| Document: | |
| DOI: | https://doi.org/10.1016/j.cpc.2023.108783 |
| Accepted Manuscript: | 1-s2.0-S0010465523001285-main.pdf |
| Supporting Documents: | |
| Supporting Datasets: | |