Decoding Nature’s Message Through the Channel of Artificial Intelligence

Abstract: Nature contains many interesting physics we want to search for, but it cannot speak them out loud. Therefore physicists need to build large particle physics experiments that encode nature’s message into experimental data. My research leverages artificial intelligence and machine learning to maximally decode nature’s message from those data. The questions I want to ask nature is: Are neutrinos Majorana particles? The answer to this question would fundamentally revise our understanding of physics and the cosmos. Currently, the most effective experimental probe for Majorana neutrino is neutrinoless double-beta decay(0vββ). Cutting-edge AI algorithms could break down significant technological barriers and, in turn, deliver the world’s most sensitive search for 0vββ. This talk will discuss one such algorithm, KamNet, which plays a pivotal role in the new result of the KamLAND-Zen experiment. With the help of KamNet, KamLAND-Zen provides a limit that reaches below 50 meV for the first time and is the first search for 0νββ in the inverted mass ordering region. Looking further, the next-generation 0vββ experiment LEGEND has created the Germanium Machine Learning group to aid all aspects of LEGEND analysis and eventually build an independent AI analysis. As the odyssey continues, AI will enlighten the bright future of experimental particle physics.

Bio: Aobo Li received his B.S. in physics at the University of Washington in 2015, then did his graduate work at Boston University as part of the KamLAND-Zen collaboration. After getting his Ph.D. in 2020, Aobo joined UNC Chapel Hill as a Postdoctoral Research Associate and COSMS Fellow. He initiates and leads the Ge Machine Learning (GeM) group, bringing AI solutions to the LEGEND and the Majorana Demonstrator experiment. Aobo has received many awards, including the American Physical Society 2023 Dissertation Award in Nuclear Physics, the UNC Postdoctoral Award of Research Excellence, and the NeurIPS 2022 ML4PS Workshop Outstanding Paper Award.