Using Machine Learning Tools to Enhance Background Subtraction at E906/SeaQuest

The SeaQuest experiment studies the flavor asymmetry of the proton through the Drell-Yan process using fixed target collisions from the 120 GeV Main Injector beam at Fermilab. Though the Drell-Yan process is clean, its cross section is minuscule compared to the nuclear cross section. This coupled with the high intensity beam yields significant random background that must be removed. Monte Carlo simulations of Drell-Yan events from the beam dump and targets have been used to develop sets of analysis cuts; Machine learning (ML) has been used to augment these cuts. This presentation will focus on using ML and analysis cuts to process the initial distributions, describe how newer, more powerful discriminating variables were developed, and give a comparison of the ML+Analysis preprocessing results with the standard analysis cuts.