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.
Highlights
In this article, we introduce a novel method to identify jets using graph networks
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
It consists of two steps: first, a shared multi-layer perceptron (MLP) is applied to each of its incoming edges, using features of the node pair connected by the edge as inputs, and produces a learned “edge feature”
Summary
The models we introduce in this article rely on the Lund plane [32] This representation provides a useful mapping of the emission phase-space to a two dimensional plane repre-. Repeating this procedure for pseudojets a and b if they contain more than one particle This procedure produces a binary Lund tree with a tuple of variables T (i) for each node i of the Lund tree, as shown in figure 1. A subset of this tree of particular significance is the primary list of tuples Lprimary containing the kinematic variables of each splitting along the primary branch of the tree, i.e. following only the pseudojet with larger transverse momentum in step 3. The righthand side of figure 2 shows the average number of nodes per jet as a function of the kt cut, which decreases quadratically as the cut is increased
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
