Abstract：
Rare event searches allow us to search for new physics inaccessible with other means by leveraging specialized radiation detectors. Machine learning provides a new tool to maximize the information provided by these detectors. The information is sparse, which forces these algorithms to start from the lowest level data and design customized models to produce results. The focus of this seminar will be on two main areas within rare event search experiments: neutrinoless double beta decay and dark matter. We will delve into the sophisticated mechanisms of radiation detectors that are specifically designed to detect these extraordinarily rare events. Moreover, the seminar will shed light on the development and application of specialized machine learning algorithms, integrating domain knowledge from fields such as spatiotemporal analysis, geometric deep learning, and time series analysis. In the latter part of the presentation, we will discuss the potential of next-generation AI/ML tools that are being developed to fully realize the discovery capabilities of rare event search experiments.
Biography:
Aobo Li is an assistant professor at UC San Diego Halicioglu Data Science Institute and Department of Physics. He received his PhD in Physics from Boston University. His work lies at the intersection between machine learning and experimental particle and nuclear physics, especially rare event search experiments. His work has received several awards including the APS Dissertation Awards in Nuclear Physics.
Host: Prof. Mengjiao Xiao
Alternative online link: https://meeting.tencent.com/dm/0sLUfBQnqUXH (id:
257683335 passcode: 123456）