Machine Learning Applications to Maintain the NuMI Neutrino Beam Quality at Fermilab

Abstract

: The NuMI target facility at Fermilab produces an intense muon neutrino beam for the NOvA (NuMI Off-axis ν e Appearance) long baseline neutrino experiment. Three arrays of muon monitors located downstream of the hadron absorber in the NuMI beamline provide the measurements of the primary beam and horn current quality. We have studied the response of muon monitors with the proton beam profile changes and focusing horn current variations. The responses of muon monitors are used to develop machine learning (ML) algorithms to monitor the beam quality. We present the development of the machine learning applications and future plans. This effort is important for future applications such as beam quality assurance, anomaly detection, and neutrino beam systematics studies. Our results demonstrate the advantages of developing useful ML applications that can be leveraged for future beamlines such as LBNF.