Speaker
Description
Normalizing Flows have been implemented across several fields, notably in image generation and recently high energy and nuclear physics. The present study investigates the ability of flow based neural networks to improve signal extraction of Λ Hyperons at CLAS12. Normalizing Flows enable density estimation by learning how to transform a simple distribution with a known PDF to a complex distribution whose PDF is unknown. These neural networks can help model complex PDFs that describe physics processes, enabling uses such as event generation. Λ 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. In future studies, when the figure of merit is unattainable, the cut on the classifier output can be made without exact precision while maintaining an optimal signal extraction; without using flows the cut must be made more precisely.
