Abstract
Jet tagging techniques that make use of deep learning show great potential for improving physics analyses at colliders. One such method is the Energy Flow Network (EFN) - a recently introduced neural network architecture that represents jets as permutation-invariant sets of particle momenta while maintaining infrared and collinear safety. We develop a variant of the Energy Flow Network architecture based on the Deep Sets formalism, incorporating permutation-equivariant layers. We derive conditions under which infrared and collinear safety can be maintained, and study the performance of these networks on the canonical example of W-boson tagging. We find that equivariant Energy Flow Networks have similar performance to Particle Flow Networks, which are superior to standard EFNs. However, equivariant Particle Flow Networks suffer from convergence and overfitting issues. Finally, we study how equivariant networks sculpt the jet mass and provide some initial results on decorrelation using planing.
Highlights
In the past half-decade, there has been a burst of activity surrounding the application of deep learning methods to a variety of problems in collider physics
The “standard candle” for jet tagging studies is the identification of boosted W bosons from their decay products, a process which we study in this work
The development of new and increasingly accurate jet taggers remains a high priority for the collider physics community
Summary
In the past half-decade, there has been a burst of activity surrounding the application of deep learning methods ( neural networks) to a variety of problems in collider physics. Multilayer perceptrons (MLPs) can be trained on lists of jets’ constituent particle momenta [12], and convolutional neural networks (CNNs) can be trained on jet images [7,8,9,10,11]. These methods have led to substantial improvements on the performances of traditional taggers. We study the performance of equivariant energy flow and particle flow networks on boosted W tagging, including some networks that use information about the jet constituent ID.
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