Speaker
肇祥 吴
(IHEP)
Description
In the large liquid scintillator detector JUNO, $\beta$-decays of $^{14}\mathrm{C}$ inevitably deposit energy within the detector, producing scintillation light. These photons can potentially overlap with positron ($e^{+}$) signals, forming pile-up events. This pile-up effect can consequently impact the fine reconstruction of the $e^{+}$ signal. We have employed three distinct machine learning models – CNN, Transformer, and KamNet – to discriminate between pile-up events and pure $e^{+}$ events. This report provides a detailed discussion of this work.
