A review on machine learning for neutrino experiments

@article{Psihas2020ARO,
  title={A review on machine learning for neutrino experiments},
  author={Fernanda Psihas and Micah Groh and Christopher Tunnell and Karl Warburton},
  journal={arXiv: Computational Physics},
  year={2020}
}
Neutrino experiments study the least understood of the Standard Model particles by observing their direct interactions with matter or searching for ultra-rare signals. The study of neutrinos typically requires overcoming large backgrounds, elusive signals, and small statistics. The introduction of state-of-the-art machine learning tools to solve analysis tasks has made major impacts to these challenges in neutrino experiments across the board. Machine learning algorithms have become an integral… 

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