Domain-Adversarial Graph Neural Networks for $Λ$ 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 $\Lambda$ 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 other experiments, such as the planned Electron Ion Collider.