Energy flow networks: deep sets for particle jets

By Patrick T. Komiske, E. Metodiev, Jesse Thaler. Journal of High Energy Physics, 2018

320 citations recorded by the source corpus.

A key question for machine learning approaches in particle physics is how to best represent and learn from collider events. As an event is intrinsically a variable-length unordered set of particles, we build upon recent machine learning efforts to learn directly from sets of features or “point clouds”. Adapting and specializing the “Deep Sets” framework to particle physics, we introduce Energy Flow Networks, which respect infrared and collinear safety by construction. We also develop Particle Flow Networks, which allow for general energy dependence and the inclusion of additional particle-level information such as charge and flavor. These networks feature a per-particle internal (latent) representation, and summing over all particles yields an overall event-level latent representation. We show how this latent space decomposition unifies existing event representations based on detector images and radiation moments. To demonstrate the power and simplicity of this set-based approach, we apply these networks to the collider task of discriminating quark jets from gluon jets, finding similar or improved performance compared to existing methods. We also show how the learned event representation can be directly visualized, providing insight into the inner workings of the model. These architectures lend themselves to efficiently processing and analyzing events for a wide variety of tasks at the Large Hadron Collider. Implementations and examples of our architectures are available online in our EnergyFlow package.

Subfields: Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/7f30230889fa7d48410dca93d4c78c5accc002e1

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In Nuclear and High Energy Physics

Citations in the corpus

Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning (2017, background): “[548], the recurrent NN image is from Ref.”
A Survey of Deep Learning for Scientific Discovery (2020, background): “…discussed, these topics span an extremely large area of research, so there are some areas, e.g. deep neural networks for set structured data [262, 113], modelling diﬀerent invariances — invariances to speciﬁed Lie groups for application to molecular property prediction [58], spherical…”
Machine learning and the physical sciences (2019, methodology): “Similarly, networks have been developed motivated by invariance to permutations on the particles presented to the network and stability to details of the radiation pattern of particles, (Komiske et al., 2018b, 2019).”
Looking Inside Jets (2019, methodology): “ly been extended into Energy Flow polynomials [146] which provide a linear basis for all infrared-andcollinear-safe jet substructure observables. These can then be used to design Energy Flow Networks [147] which are QCD-motivated machine-learning substructure tools. CHAPTER 5. JET SUBSTRUCTURE: CONC”
Graph neural networks in particle physics (2020, methodology): “In contrast the authors of [51] used a DeepSet architecture, where each node representation is created independently from its neighbors, the node representations are then summed to create the graph representation, with each node representation weighted by the particles energy.”