{"paper":{"title":"Domain-Adversarial Graph Neural Networks for $\\Lambda$ Hyperon Identification with CLAS12","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["nucl-ex"],"primary_cat":"hep-ex","authors_text":"Anselm Vossen, Matthew McEneaney","submitted_at":"2023-02-10T19:32:35Z","abstract_excerpt":"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 $\\Lambda$ hyperon events with the CLAS12 experiment at Jefferson Lab. The GNN method we have developed increases the purity of the $\\Lambda$ 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 $\\Lambda$ and other channels at CLAS12 and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.05481","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2302.05481/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}