Jet tagging in the Lund plane with graph networks

Abstract

The identification of boosted heavy particles such as top quarks or vector bosons is one of the key problems arising in experimental studies at the Large Hadron Collider. In this article, we introduce LundNet, a novel jet tagging method which relies on graph neural networks and an efficient description of the radiation patterns within a jet to optimally disentangle signatures of boosted objects from background events. We apply this framework to a number of different benchmarks, showing significantly improved performance for top tagging compared to existing state-of-the-art algorithms. We study the robustness of the LundNet taggers to non-perturbative and detector effects, and show how kinematic cuts in the Lund plane can mitigate overfitting of the neural network to model-dependent contributions. Finally, we consider the computational complexity of this method and its scaling as a function of kinematic Lund plane cuts, showing an order of magnitude improvement in speed over previous graph-based taggers.

Higgs boson tagging with the Lund jet plane

“…We have concentrated on two transverse momentum benchmark scenarios, namely moderate and high boost of the Higgs boson. Inspired by previous work on W and top tagging using the Lund jet plane [22,26], we have built images that are used as inputs to a convolutional neural network for classification. We have compared the performance of this tagger to a more standard approach: a single-variable analysis that exploits a theoreticalmotivated observable, namely the jet color ring.…”

“…Furthermore, the use of other ML architectures, such as, e.g. graph neural networks, may lead to a further gain in the classification performance [26]. We plan to explore these directions in the future work.…”

“…Recently, the all-order structure of the Lund jet plane density has also been computed [25]. The Lund jet plane has been used together with Long Short-Term Memory (LSTM) networks and, more recently, with graph neural networks for W boson and top tagging [22,26]. It is also a plausible choice for event generation using generative models [27].…”

Identifying the Quantum Properties of Hadronic Resonances using Machine Learning

“…The goal of this paper is to investigate the potential of tagging the quantum numbers of hadronically decaying resonances using all of the available information inside the resulting jet. There are now many applications of deep learning to jet substructure [10] with a variety of jet representations used for the learning (images [11][12][13][14][15][16][17][18], ordered lists [19][20][21][22][23], trees [24,25], graphs [26][27][28][29][30][31], unordered sets [32][33][34], observable bases [35][36][37][38][39], etc. [40][41][42] -see Ref.…”

Lund jet plane for Higgs tagging

“…We consider Lund jet images and Convolutional Neural Networks (CNN) 1 for the signal and background classification for hadronically decaying boosted Higgs boson [3]. The Lund jet plane is a theory inspired jet representation which is used for jet tagging recently [4,5]. It uses the information of the radiation pattern inside the jet and is a log-log (ln 1 ∆ , ln…”