Oct 19 – 23, 2020
Europe/Zurich timezone

Zero-Permutation Jet Parton Assignment

Oct 21, 2020, 4:10 PM
Regular talk 2 ML for analysis : Application of Machine Learning to analysis, event classification and fundamental parameters inference Workshop


Seungjin Yang (University of Seoul, Department of Physics (KR))


For many top quark measurements, it is essential to reconstruct the top quark from its decay products. For example, the top quark pair production process in the all-jets final state has six jets initiated from daughter partons and additional jets from initial/final state radiation. Due to the many possible permutations, it is very hard to assign jets to partons. We use a deep neural network with an attention-based architecture together with a new objective function to the jet-parton assignment problem. Our novel deep learning model and the physics-inspired objective function enable jet-parton assignment with high-dimensional data while the attention mechanism bypasses the combinatorial explosion that usually leads to intractable computational requirements. The model can also be applied as a classifier to reject the overwhelming QCD background, showing increased performance over standard classification methods.

Primary authors

Seungjin Yang (University of Seoul, Department of Physics (KR)) Jason Lee (University of Seoul (KR)) Inkyu Park (University of Seoul, Department of Physics (KR)) Ian James Watson (University of Seoul)

Presentation materials