Bulletin of the American Physical Society
6th Joint Meeting of the APS Division of Nuclear Physics and the Physical Society of Japan
Sunday–Friday, November 26–December 1 2023; Hawaii, the Big Island
Session 1WFB: AI in Nuclear Physics Experiments IIInvited Workshop
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Chair: Itaru Shimizu Room: Hilton Waikoloa Village Queens 5 |
Sunday, November 26, 2023 11:00AM - 11:30AM |
1WFB.00001: Machine learning status and prospects in KamLAND-Zen Invited Speaker: Hideyoshi Ozaki KamLAND-Zen is a neutrinoless double-beta (0nbb) decay search experiment using a large liquid scintillator detector. The energy resolution is extremely important for reducing the background of two neutrino double-beta decay, and the vertex resolution is necessary for reducing the gamma decay background from radioactive impurities originating from the detector components such as an inner balloon. To improve these, the development of a reconstruction tool using a Graph Neural Network is underway. Furthermore, the spallation of double-beta decay isotope: xenon nuclei by muons generates long-lived unstable nuclei, which has become a major background in the current search for 0nbb decay. As it cannot be completely removed by the delayed coincidence measurement, particle identification between beta and gamma for removal is expected. For this reason, the development of PID using machine learning, including KamNet, is in progress. |
Sunday, November 26, 2023 11:30AM - 12:00PM |
1WFB.00002: Deep Neural Networks for Simulation, Reconstruction, and Analysis of EXO-200 data Invited Speaker: Igor Ostrovskiy EXO-200 was an experiment whose primary goal was to search for the neutrinoless double-beta decay of Xe-136. During its lifetime, the experiment accumulated unique datasets of physics and calibration data that remain to be a perfect testbed for advanced analysis techniques, such as deep neural networks (DNNs). After introducing the EXO-200 experiment, this talk describes successful applications of DNNs to simulate, reconstruct, and analyze the EXO-200 data. We show that a DNN is able to extract necessary high-level information directly from raw digitized waveforms, with minimal pre-processing. The accuracy of the developed algorithms is presented and compared to what was achieved by the conventional approaches. Importantly, the methods described in this talk are validated by (and in some cases are trained on) the real detector data, either reducing or eliminating the reliance on the Monte Carlo and its imperfections. The talk concludes by discussing advantages and challenges of DNNs for the upcoming low-background experiments, in particular nEXO and DARWIN. |
Sunday, November 26, 2023 12:00PM - 12:30PM |
1WFB.00003: Deep Learning for Water Cherenkov Detectors Invited Speaker: Junjie Xia Cherenkov radiation is widely used in particle physics and astrophysics since its discovery in the early 20th century. Numerous water Cherenkov detectors have been deployed, with more in preparation, for various physics programs such as nucleon decay search and precise neutrino measurements. Like all other experiments, efficiently quantifying detector systematic uncertainties poses a significant challenge due to their intricate impacts on the observed physics. This challenge becomes even more crucial in the next generation experiments, where extensive data statistics will make the systematic effects the dominant uncertainties. Thankfully, the rapid advancements in artificial intelligence and deep learning offer promising solutions to tackle these challenges. |
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