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ABSTRACT: This talk will focus on the application of Graph Neural Networks (GNNs), specifically ParticleNet, in physics analysis at BESIII. It will explore their effectiveness in studying Lambda_c semileptonic decays and hadronic decays, leveraging the power of GNNs to analyze data with complex relational structures. We will also try to discuss the systematic uncertainry treatment， which is still an open question in experimental particle physic. Finally, this talk also showcases the promise of GNNs in advancing physics analysis at BESIII and lays the foundation for further integration of these techniques in future research
About the speaker: Yunxuan Song is a scientist affiliated with the École Polytechnique Fédérale de Lausanne(EPFL). He is actively involved in the LHCb and BESIII experiments. His research interests encompass rare decays, charm physics, and exotics. In 2022, Yunxuan completed his doctoral studies at Peking University and was honored with BESIII PhD Thesis Award in 2022, where he was jointly trained by PKU and UCAS.
Zoom Link: https://zoom.us/j/92554699891?pwd=bldBZVhoMG9neDFIZXcxMHp2YzZ5Zz09
Zoon ID: 92554699891
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