A jet tagging algorithm of graph network with HaarPooling message passing. (arXiv:2210.13869v3 [hep-ex] UPDATED)

Recently methods of graph neural networks (GNNs) have been applied to solving
the problems in high energy physics (HEP) and have shown its great potential
for quark-gluon tagging with graph representation of jet events. In this paper,
we introduce an approach of GNNs combined with a HaarPooling operation to
analyze the events, called HaarPooling Message Passing neural network (HMPNet).
In HMPNet, HaarPooling not only extract the features of graph, but also embed
additional information obtained by clustering of k-means of …

algorithm arxiv graph network tagging