Improving $\Lambda$ Signal Extraction with Domain Adaptation via Normalizing Flows

arXiv:2403.14076v1 Announce Type: cross
Abstract: 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 …

abstract application arxiv cs.lg domain domain adaptation extraction flow functions hep-ex improving lambda networks neural networks novel physics probability processes signal study type via