A Review on Machine Learning for Neutrino Experiments

Abstract

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 tool of neutrino physics, and their development is of great importance to the capabilities of next generation experiments. An understanding of the roadblocks, both human and computational, and the challenges that still exist in the application of these techniques is critical to their proper and beneficial utilization for physics applications. This review presents the current status of machine learning applications for neutrino physics in terms of the challenges and opportunities that are at the intersection between these two fields.

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Cited by in corpus (11)

• Applications and Techniques for Fast Machine Learning in Science
• A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatory
• Application of Transfer Learning to Neutrino Interaction Classification
• Detector signal characterization with a Bayesian network in XENONnT
• Neutrino Characterisation using Convolutional Neural Networks in CHIPS water Cherenkov detectors
• Interpretable Boosted Decision Tree Analysis for the Majorana Demonstrator
• The use of Boosted Decision Trees for Energy Reconstruction in JUNO experiment
• Predicting the Masses of Exotic Hadrons with Data Augmentation Using Multilayer Perceptron
• Using Machine Learning to Separate Cherenkov and Scintillation Light in Hybrid Neutrino Detector
• Contrastive Learning for Robust Representations of Neutrino Data
• Neutral pion reconstruction using machine learning in the MINERvA experiment at $⟨E_{\nu }⟩\sim 6$ GeV