Vision for QCD to the 2030s and Beyond

Sterman, George

doi:10.48550/arxiv.2108.07950

Cited by 1 publication

(1 citation statement)

References 54 publications

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“…Considerable attention to the characteristics of jets is required for a holistic search strategy. Jetsubstructure [31][32][33][34] is a sub-area of LHC physics that has received significant investigation both from a theory-driven perspective [35][36][37][38][39][40][41] as well as the data-driven paradigm [42][43][44][45][46][47][48][49]. It focuses on distinguishing rare hadronic decays of heavy boosted particles (signal) like the top-quark and Higgs boson from those originating from the abundantly produced (background) gluons and lighter quarks.…”

Section: Introductionmentioning

confidence: 99%

Energy-weighted message passing: an infra-red and collinear safe graph neural network algorithm

Konar

Ngairangbam

Spannowsky

2022

J. High Energ. Phys.

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Hadronic signals of new-physics origin at the Large Hadron Collider can remain hidden within the copiously produced hadronic jets. Unveiling such signatures require highly performant deep-learning algorithms. We construct a class of Graph Neural Networks (GNN) in the message-passing formalism that makes the network output infra-red and collinear (IRC) safe, an important criterion satisfied within perturbative QCD calculations. Including IRC safety of the network output as a requirement in the construction of the GNN improves its explainability and robustness against theoretical uncertainties in the data. We generalise Energy Flow Networks (EFN), an IRC safe deep-learning algorithm on a point cloud, defining energy weighted local and global readouts on GNNs. Applying the simplest of such networks to identify top quarks, W bosons and quark/gluon jets, we find that it outperforms state-of-the-art EFNs. Additionally, we obtain a general class of graph construction algorithms that give structurally invariant graphs in the IRC limit, a necessary criterion for the IRC safety of the GNN output.

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