Speaker
Dmitrii Kobylianskii
(Weizmann Institute of Science (IL))
Description
Tree structure is a natural way to represent particle decays in high energy physics. The possibility of reconstructing the entire decay tree that ends in stable particles entering the detector is an interesting and potentially beneficial task. [The interesting and extremely helpful task is to reconstruct the entire decay process, starting from the leaf nodes, which are the reconstructed particles.] We propose a graph-based neural network for tree reconstruction using truth-level particles as a starting point. The proposed model’s performance was evaluated on the toy Phasespace dataset and realistic Pythia8 simulations of light quark decay chains.
Author
Dmitrii Kobylianskii
(Weizmann Institute of Science (IL))
Co-authors
Eilam Gross
(Weizmann Institute of Science (IL))
Etienne Dreyer
(Weizmann Institute of Science (IL))
Mr
Junjian Lu
(MBUZAI)
Dr
Shangsong Liang
(MBUZAI)
Mr
Siwei Liu
(MBUZAI)
