报告摘要：

Neutrinoless Double Beta Decay(0νββ) is one of the major research interests in neutrino physics. The discovery of 0νββ would answer persistent puzzles in the standard model. Liquid scintillator-based detectors are one of the leading detector technologies in the search for neutrinos. They are currently limited by naturally occurring and spallation induced backgrounds. With the advancements in machine learning and computer vision, we designed model to recognize neutrinos based on Spherical Convolutional Neural Network. We manage to reject backgrounds in Monte Carlo data that are previously considered "impossible", which leads to a non-trivial sensitivity boost without hardware update. With the advancement in this field, we are looking forward to adopting more sophisticated algorithm, and tackle harder problems including directionality reconstruction.

报告人简介：

李奥博是中微子研究领域的学术新秀。他率先在大型液闪探测器KamLAND-Zen中引入深度神经网络，使得KamLAND-Zen探测器获得了无中微子双beta衰变能区的粒子鉴别性能力。他还首次把最新的全视角计算机视觉方法Spherical CNN引入球形中微子探测器，近一步提高了粒子鉴别性能。

“论坛”主请人联系方式：

续本达  orv@tsinghua.edu.cn