Perry Siehien

Document Type


Date of Award


Degree Name

Master of Science (MS)



First Advisor

Jing Liu


Neutrinos are subatomic particles that weakly interact with matter due to their neutral charge and small cross section. Detectors that search for neutrinos require sensitive instrumentation, which makes them susceptible to various background sources such as gamma rays. Additionally, coherent elastic neutrino-nucleus scattering events, or CEvNS, are the weakest neutrino interactions at 1-25 keV, making them exceptionally difficult to observe. To understand the physics of CEvNS events within the detector material, the recoil signatures of relevant interactions must be determined. Traditional analysis methods are effective, but cannot be applied to energies below 50 keV, due to the overlap of discrimination criteria. In this thesis, we investigate the effectiveness of applied neural networks to distinguish between two recoil signatures present in COHERENT’s CENNS-10 LAr detector. Results indicate that dense neural networks perform well on classifying low energy events that approach the CEvNS energy threshold. We also discuss modifications to the complexity and structure of the neural network, which may improve generality.

Subject Categories

Computer Sciences | Elementary Particles and Fields and String Theory | Physics


Detector, Liquid Argon, Machine Learning, Neural Networks, Neutrinos

Number of Pages



University of South Dakota



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