Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders

Abstract

We study how to use Deep Variational Autoencoders for a fast simulation of jets of particles at the LHC. We represent jets as a list of constituents, characterized by their momenta. Starting from a simulation of the jet before detector effects, we train a Deep Variational Autoencoder to return the corresponding list of constituents after detection. Doing so, we bypass both the time-consuming detector simulation and the collision reconstruction steps of a traditional processing chain, speeding up significantly the events generation workflow. Through model optimization and hyperparameter tuning, we achieve state-of-the-art precision on the jet four-momentum, while providing an accurate description of the constituents momenta, and an inference time comparable to that of a rule-based fast simulation.

11 pages, 8 figures

References in corpus (4)

• An Introduction to PYTHIA 8.2
• Generative Adversarial Networks
• Particle-flow reconstruction and global event description with the CMS detector
• Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics

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