26 citations · 48 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
Quark Gluon Jet Discrimination with Weakly Supervised Learning
Jason Sang Hun Lee, Sang Man Lee, Yunjae Lee +3
Deep learning techniques are currently being investigated for high energy physics experiments, to tackle a wide range of problems, with quark and gluon discrimination becoming a be…
Quark-Gluon Jet Discrimination Using Convolutional Neural Networks
Jason Sang Hun Lee, Inkyu Park, Ian James Watson +1
Currently, newly developed artificial intelligence techniques, in particular convolutional neural networks, are being investigated for use in data-processing and classification of…
Zero-Permutation Jet-Parton Assignment using a Self-Attention Network
Jason Sang Hun Lee, Inkyu Park, Ian James Watson +1
In high-energy particle physics events, it can be advantageous to find the jets associated with the decays of intermediate states, for example, the three jets produced by the hadro…