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

TL;DR

This paper introduces a deep variational autoencoder approach for rapid jet simulation at the LHC, significantly reducing computational time while maintaining high accuracy in jet constituent and momentum predictions.

Contribution

The paper presents a novel application of deep variational autoencoders to simulate particle jets efficiently, bypassing traditional detector simulation and reconstruction steps.

Findings

Achieves state-of-the-art precision on jet four-momentum

Provides accurate constituent momentum distributions

Offers inference times comparable to rule-based methods

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.