Rare event searches allow us to search for new physics at energy scales 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. This seminar aims to delve into two primary contenders within rare event search experiments: neutrinoless double beta decay and WIMP dark matter. We will explore mechanisms of radiation detectors specifically engineered to identify these exceedingly rare occurrences. Additionally it will illuminate the development and application of customized machine learning algorithms adept at efficiently analyzing data obtained from these radiation detectors. Towards the end of this presentation attention will shift to the potential leverage of fast machine learning techniques in augmenting the efficacy 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/nuclear physics especially rare event search experiments. He has received several awards including APS Dissertation Awards in Nuclear Physics.