SPA-Net, a New Perspective for Parton-Jet Assignment?
Ta-Wei Ho1*, Michael James Fenton2, Alexander Shmakov3, Shih-Chieh Hsu4, Daniel Whiteson2
1Department of Physics, National Tsing Hua University, Hsinchu City, Taiwan
2Department of Physics and Astronomy, University of California Irvine, California, USA
3Department of Computer Science, University of California Irvine, California, USA
4Department of Physics and Astronomy, University of Washington, Washington, USA
* Presenter:Ta-Wei Ho,
The top quarks produced by pp collision in Large Hadron Collider (LHC), have a very complicated process still can't be well-classified today. In this project, we present a novel approach to the “all hadronic decay” process of Top quarks base on the neural networks with attention mechanism, we call it “Symmetry Preserving Attention Networks”(SPA-Net). This network identifies the decay products of each quarks unambiguously and without combinatorial explosion. This approach performs an outstanding result compare to the existing state-of-the-art method. Our network can correctly assign all hadronic decay in 93.0% of 6 jets, 87.8% of 7 jets, and 82.6% of  8 jets event respectively. This work is submitted to PRL (arXiv: 2010.09206)

Keywords: Machine Learning, Parton-Jet assignment, Top-pair production, Attention network