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TRACK RECONSTRUCTION ON EMPHATIC SPECTROMETER USING MACHINE LEARNING

Abstract

Neutrinos are the most abundant massive fundamental particle universe, but because they interact with other matter only through the weak nuclear force, we know very little about them. The weak interaction decay is very well understood, however the process of creating some of the hadrons is only understood at the 10-40% level, resulting in an uncertainty in the neutrino flux at accelerators at the level of 10%. More measurements of the particle interactions (hadron interactions) that create neutrinos can help researchers reduce the uncertainty on the neutrino flux, and will enhance the capabilities of neutrino experiments like NOvA and DUNE in a variety of measurements such as neutrino cross-sections, sterile neutrino searches, and other BSM physics searches. The EMPHATIC collaboration's goal is to measure these hadron production probabilities (cross sections) using a novel compact, table-top sized spectrometer. This talk describes the development of a machine learning algorithm to reconstruct particle tracks from the EMPHATIC spectrometer.

Acknowledgements

Kennesaw State University and Fermilab

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