Energy Flow Networks: Deep Sets for Particle Jets

by   Patrick T. Komiske, et al.

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. 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.


Machine Learning for Particle Flow Reconstruction at CMS

We provide details on the implementation of a machine-learning based par...

QCD-Aware Recursive Neural Networks for Jet Physics

Recent progress in applying machine learning for jet physics has been bu...

Progress towards an improved particle flow algorithm at CMS with machine learning

The particle-flow (PF) algorithm, which infers particles based on tracks...

Partition Pooling for Convolutional Graph Network Applications in Particle Physics

Convolutional graph networks are used in particle physics for effective ...

GANplifying Event Samples

A critical question concerning generative networks applied to event gene...

Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics

A good feature representation is a determinant factor to achieve high pe...

Unsupervised Image Representation Learning with Deep Latent Particles

We propose a new representation of visual data that disentangles object ...

Please sign up or login with your details

Forgot password? Click here to reset