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Energy flow networks: deep sets for particle jets

Komiske, Patrick T. ; Metodiev, Eric M. ; Thaler, Jesse

The journal of high energy physics, 2019-01, Vol.2019 (1), p.1-46, Article 121 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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Título:
Energy flow networks: deep sets for particle jets
Autor: Komiske, Patrick T. ; Metodiev, Eric M. ; Thaler, Jesse
Assuntos: Artificial intelligence ; Classical and Quantum Gravitation ; CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS ; Dependence ; Elementary Particles ; Energy flow ; Flavor (particle physics) ; High energy physics ; Jets ; Large Hadron Collider ; Machine learning ; Networks ; Particle physics ; Physics ; Physics and Astronomy ; PHYSICS OF ELEMENTARY PARTICLES AND FIELDS ; QCD Phenomenology ; Quantum Field Theories ; Quantum Field Theory ; Quantum Physics ; Regular Article - Theoretical Physics ; Relativity Theory ; Representations ; String Theory ; Three dimensional models
É parte de: The journal of high energy physics, 2019-01, Vol.2019 (1), p.1-46, Article 121
Notas: SC0011090; SC0012567
USDOE Office of Science (SC), High Energy Physics (HEP)
Descrição: A bstract 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.
Editor: Berlin/Heidelberg: Springer Berlin Heidelberg
Idioma: Inglês

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Energy flow networks: deep sets for particle jets

Komiske, Patrick T. ; Metodiev, Eric M. ; Thaler, Jesse

Berlin/Heidelberg: Springer Berlin Heidelberg

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