Over the past few decades, significant advances have been made in understanding the fundamental properties of neutrinos, leading to groundbreaking measurements and discoveries. However, challenges persist, including to determine the CP-violation phase $\delta_{\mathrm{CP}}$, which remains experimentally elusive. The leading causes of these challenges include the complexity of systematic uncertainties from various sources, which intricately influence the observed physics in neutrino experiments.
In recent years, AI and machine learning (ML) techniques have achieved remarkable success across a variety of applications and research domains, making them strong candidates for addressing the challenges of event reconstruction in modern neutrino experiments. Early applications of AI/ML in neutrino physics demonstrate significant potential for tasks such as feature recognition, fast surrogates, and differentiable simulation. Looking ahead, a fully AI/ML-based ``bias-free” experimental analysis may be possible within a few years if critical issues such as systematic uncertainty quantification and self-supervised representation learning are successfully resolved.
Join from PC, Mac, Linux, iOS or Android: https://stanford.zoom.us/j/98973156241?pwd=cEU5RFdlVXoyc0JTeTlDMkozKzQ5UT09
David Charles Goldfinger, Zhi Zheng
(dgoldfinger@stanford.edu, zzheng@slac)
