3 papers
hep-ex2026
Mixture Density Networks for Neutrino Reconstruction at Hadron Colliders
Seungjin Yang, Jason S. H. Lee, Junghwan Goh
Neutrino momentum reconstruction at hadron colliders is intrinsically ambiguous because the longitudinal momentum is not directly observed. We study this problem in semileptonic $t…
hep-ph2025
Identification of flavor-changing neutral current interactions using machine learning techniques
Byeonghak Ko, Jeewon Heo, Woojin Jang +4
Flavor-changing neutral currents (FCNCs) are forbidden at tree level in the Standard Model (SM), but they can be enhanced in physics Beyond the Standard Model (BSM) scenarios.In th…
hep-ph2025
Improving the Direct Determination of using Deep Learning
Jeewon Heo, Woojin Jang, Jason Sang Hun Lee +3
An -jet tagging approach to determine the Cabibbo-Kobayashi-Maskawa matrix component directly in the dileptonic final state events of the top pair production in proto…