STI Publications - View Publication Form #17582
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Publication Information
| Title | Domain-Adversarial Graph Neural Networks for Lambda Hyperon Identification with CLAS12 | ||||
| Abstract | Machine learning methods and in particular Graph Neural Networks (GNNs) have revolutionized many tasks within the high energy physics community. We report on the novel use of GNNs and a domain-adversarial training method to identify ? hyperon events with the CLAS12 experiment at Jefferson Lab. The GNN method we have developed increases the purity of the ? yield by a factor of 1.95 and by 1.82 using the domain-adversarial training. This work also provides a good benchmark for developing event tagging machine learning methods for the ? and other channels at CLAS12 and other experiments, such as the planned Electron Ion Collider. | ||||
| Author(s) | Matthew McEneaney, Anselm Vossen  |
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| Publication Date | June 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 | Journal of Instrumentation |
| Refereed | Yes |
| Volume | 18 |
| Issue | |
| Page(s) | P06002 |
Attachments/Datasets/DOI Link
| Document(s) |
2302.05481.pdf
(STI Document)
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| DOI Link | |
| Dataset(s) | (none) |
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