Improving $\Lambda$ Signal Extraction with Domain Adaptation via Normalizing Flows
<|reference_start|>Improving $\Lambda$ Signal Extraction with Domain Adaptation via Normalizing Flows: The present study presents a novel application for normalizing flows for domain adaptation. The study investigates the ability of flow based neural networks to improve signal extraction of $\Lambda$ Hyperons at CLAS12. Normalizing Flows can help model complex probability density functions that describe physics processes, enabling uses such as event generation. $\Lambda$ signal extraction has been improved through the use of classifier networks, but differences in simulation and data domains limit classifier performance; this study utilizes the flows for domain adaptation between Monte Carlo simulation and data. We were successful in training a flow network to transform between the latent physics space and a normal distribution. We also found that applying the flows lessened the dependence of the figure of merit on the cut on the classifier output, meaning that there was a broader range where the cut results in a similar figure of merit.<|reference_end|>
@article{kelleher2024improving,
    title={Improving $\Lambda$ Signal Extraction with Domain Adaptation via
  Normalizing Flows},
    author={Rowan Kelleher, Matthew McEneaney, Anselm Vossen},
    journal={arXiv preprint arXiv:2403.14076},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14076},
    primaryClass={hep-ex cs.LG}
}