English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Recently methods of graph neural networks (GNNs) have been applied to solvingthe problems in high energy physics (HEP) and have shown its great potentialfor quark-gluon tagging with graph representation of jet events. In this paper,we introduce an approach of GNNs combined with a HaarPooling operation toanalyze the events, called HaarPooling Message Passing neural network (HMPNet).In HMPNet, HaarPooling not only extracts the features of graph, but embedsadditional information obtained by clustering of k-means of different particlefeatures. We construct Haarpooling from five different features: absoluteenergy $\log E$, transverse momentum $\log p_T$, relative coordinates$(\Delta\eta,\Delta\phi)$, the mixed ones $(\log E, \log p_T)$ and $(\log E,\log p_T, \Delta\eta,\Delta\phi)$. The results show that an appropriateselection of information for HaarPooling enhances the accuracy of quark-gluontagging, as adding extra information of $\log P_T$ to the HMPNet outperformsall the others, whereas adding relative coordinates information$(\Delta\eta,\Delta\phi)$ is not very effective. This implies that by addingeffective particle features from HaarPooling can achieve much better resultsthan solely pure message passing neutral network (MPNN) can do, whichdemonstrates significant improvement of feature extraction via the poolingprocess. Finally we compare the HMPNet study, ordering by $p_T$, with otherstudies and prove that the HMPNet is also a good choice of GNN algorithms forjet tagging.

Jet tagging algorithm of graph network with HaarPooling message passing