Domain-adversarial graph neural networks for Λ hyperon identification with CLAS12

Journal Article · 31 May 2023 · Journal of Instrumentation

DOI: https://doi.org/10.1088/1748-0221/18/06/p06002 · OSTI ID:2006986

McEneaney, M. ^[1]; Vossen, A. ^[2]

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1. Duke Univ., Durham, NC (United States)
2. Duke Univ., Durham, NC (United States); Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)

Machine learning methods and in particular Graph Neural Networks (GNNs) have revolutionized many tasks within the high energy physics community. Particularly in the realm of jet tagging, GNNs and domain adaptation have been especially successful. However, applications with lower energy events have not received as much attention. Here, we report on the novel use of GNNs and a domain-adversarial training method to identify Λ hyperon events with the CLAS12 experiment at Jefferson Lab. The GNN method we have developed increases the purity of the Λ 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 Λ and other channels at CLAS12 and other experiments, such as the planned Electron Ion Collider.

• Research Organization: Thomas Jefferson National Accelerator Facility, Newport News, VA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Nuclear Physics (NP)
• Grant/Contract Number: AC05-06OR23177
• OSTI ID: 2006986
• Report Number(s): DOE/OR/23177--5712; JLAB-PHY--23-3755; arXiv:2302.05481
• Journal Information: Journal of Instrumentation, Journal Name: Journal of Instrumentation Journal Issue: 06 Vol. 18; ISSN 1748-0221
• Publisher: Institute of Physics (IOP)Copyright Statement
• Country of Publication: United States
• Language: English