Toward Next-generation Artificial Intelligence for Neutrino Experiments
Abstract: The investigation of neutrinos would answer persistent puzzles in the universe and open the gate to physics beyond the standard model. Unfortunately, those imperceptible particles are extremely tough to detect since they barely interact with matters. As we enter the era of artificial intelligence, machine learning has grown exponentially in almost all types of neutrino detectors. Thanks to its end-to-end nature, machine learning algorithms can easily surpass traditional algorithms by maximally extracting information from detectors.
In this talk, I will discuss the evolution of AI algorithms to accelerate the discovery of neutrino experiments. Starting from canonical event classification algorithms, we will follow the path to explore interpretable machine learning and label-free self-supervised learning. This will eventually lead to extensible, task-agnostic next-generation AI: the foundation model.
The College of Arts