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- W4293783553 abstract "This effort is important for future applications such as beam quality assurance, anomaly detections, neutrino beam systematics studies and neutrino beam quality assurance. Our results demonstrate the advantages of developing useful ML applications that can be leveraged for future beamlines such as LBNF." @default.
- W4293783553 created "2022-08-31" @default.
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- W4293783553 date "1900-01-01" @default.
- W4293783553 modified "2023-09-27" @default.
- W4293783553 title "Machine Learning Applications to Maintain the NuMI Neutrino Beam Quality at Fermilab" @default.
- W4293783553 doi "https://doi.org/10.2172/1883583" @default.
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