Abstract: The discovery of Majorana neutrinos would fundamentally revise our understanding of physics and the cosmos. Currently, the most effective experimental probe of the Majorana neutrinos is neutrinoless double-beta decay(0vββ). Meanwhile, the explosive growth of artificial intelligence (AI) over the last decade has brought new opportunities to 0vββ experiments. Next-generation AI algorithms could break down significant technological barriers and 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 pursue a complete AI analysis chain. As the odyssey continues, AI will enlighten the bright future of 0νββ and fundamental symmetries in general.

Bio: Aobo Li received his B.S. in physics at the University of Washington, then did his graduate work at Boston University as part of the KamLAND-Zen collaboration. After getting his Ph.D., Aobo joined UNC Chapel Hill as a Postdoctoral Research Associate and COSMS Fellow.