Optimizing Dimuon Reconstruction in SpinQuest*

The SpinQuest experiment at Fermilab presents an excellent opportunity to apply machine learning methods for enhanced data analysis. SpinQuest aims to measure the correlation between the angular distribution of final-state dimuons and the transverse polarization of the target. Accurate reconstruction of the dimuons' four-momentum, originating from the target, is crucial for this measurement. To achieve this, the University of Virginia has developed an innovative approach to reconstruction called Q-Tracker, using a series of deep neural networks. The commissioning and early production runs of SpinQuest taken in the Spring of 2024 offered first-hand experience of both Q-Tracker's online and offline reconstruction abilities. In this talk, we will present the implementation of Q-Tracker for SpinQuest, and review analysis for online and offline reconstruction with the ambition to extract multiple polarized Transverse Momentum Distributions with future data.