Longitudinal Spin Transfer to $\Lambda$ Hyperons in CLAS12

TL;DR

This paper reports on measuring the longitudinal spin transfer to $\\Lambda$ hyperons using CLAS12 data, aiming to understand $\\Lambda$ spin structure, and introduces the use of Graph Neural Networks for event identification.

Contribution

It presents the ongoing analysis of $D_{LL'}$ in $\\Lambda$ hyperons with new machine learning techniques, advancing the experimental understanding of hyperon spin structure.

Findings

Initial results on $D_{LL'}$ measurement using CLAS12 data.

Implementation of Graph Neural Networks for event classification.

Potential to discriminate between models of $\\Lambda$ spin structure.

Abstract

Using the self-analyzing decay of the $\Lambda$, the longitudinal spin transfer $D_{LL'}$ to the hyperon from a polarized electron beam scattering off an unpolarized proton target can be determined. For $\Lambda$s produced in the current fragmentation region, this quantity is proportional to the helicity dependent fragmentation function $G_1^\Lambda$ and can provide insight into the spin structure of the $\Lambda$. Currently, limited experimental data on $D_{LL'}$ cannot discriminate between different models of $\Lambda$ spin structure. This contribution reports on the status of the ongoing analysis of the longitudinal spin transfer using data taken by the CLAS12 spectrometer at Jefferson Lab with a 10.6 GeV polarized electron beam. We also report on the novel use of Graph Neural Networks (GNNs) to identify signal events.