Date Thesis Awarded


Access Type

Honors Thesis -- Open Access

Degree Name

Bachelors of Science (BS)




David S. Armstrong

Committee Members

Keith Griffioen

Todd Averett

Chad Vance


The MOLLER Experiment at Jefferson Lab intends to make a precise measurement of the weak charge of the electron through parity-violating electron scattering. To achieve the level of precision required for the experiment, background rates of events other than electron-electron scattering must be known. Working with data from Monte-Carlo simulations created using a GEANT4 simulation package, I show that the combined signal from two existing detector subsystems of the MOLLER experiment allow for particle identification between electron and pion events. I worked to optimize an additional ‘Pion Exit Scintillator’ which improves the ability to distinguish particle identity at the cost of a large fraction of pion events. This identification ability is used to develop a machine learning based algorithm which is intended for use in the experimental determination of pion scattering asymmetry and the dilution of the electron signal in the main detector in counting mode measurements. The best trained classifier correctly classifies 95.1% of events.

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