A Review on Machine Learning for Neutrino Experiments

TL;DR

This review discusses how machine learning techniques have significantly advanced neutrino experiments by addressing challenges like background noise and small data sets, highlighting current applications, challenges, and future opportunities.

Contribution

It provides a comprehensive overview of machine learning applications in neutrino physics, emphasizing current challenges and future prospects.

Findings

Machine learning has become essential in neutrino data analysis.

Significant improvements in signal detection and background reduction.

Ongoing challenges include computational complexity and interpretability.

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.