Application of Machine Learning Methods in Neutrino Experiments

Machine learning (ML) methodologies, in recent years, have carved a significant niche within the realm of neutrino physics. These tools are not just ancillary but pivotal to the success of emerging experimental endeavors in the field. An overwhelming portion of current neutrino experiments rely heavily on an array of state-of-the- art machine learning tools. These sophisticated ML tools have empowered researchers to derive physical insights of unparalleled depth and clarity, significantly outpacing the potential of traditional data analytics approaches. Central to this achievement are the advanced neural network designs being employed. With the rapid evolution of machine learning techniques across diverse sectors, it is of paramount importance for neutrino physicists to remain updated and adapt these innovations seamlessly into their research workflows. The pace and vigor of advancements in machine learning, especially its applications in neutrino research, are nothing short of remarkable. Given this dynamic landscape, there's a continuous imperative to periodically distill, synthesize, and keep tabs on emerging trends. This paper, in essence, is an endeavor to provide such a synthesized overview, capturing the zeitgeist of ML applications in neutrino physics