LHC Hadronic Jet Generation Using Convolutional Variational Autoencoders with Normalizing Flows

Abstract

In high energy physics, one of the most important processes for collider data analysis is the comparison of collected and simulated data. Nowadays the state-of-the-art for data generation is in the form of Monte Carlo (MC) generators. However, because of the upcoming high-luminosity upgrade of the LHC, there will not be enough computational power or time to match the amount of needed simulated data using MC methods. An alternative approach under study is the usage of machine learning generative methods to fulfill that task.Since the most common final-state objects of high-energy proton collisions are hadronic jets, which are collections of particles collimated in a given region of space, this work aims to develop a convolutional variational autoencoder (ConVAE) for the generation of particle-based LHC hadronic jets. Given the ConVAE's limitations, a normalizing flow (NF) network is coupled to it in a two-step training process, which shows improvements on the results for the generated jets. The ConVAE+NF network is capable of generating a jet in $18.30\pm 0.04\mu$s, making it one of the fastest methods for this task up to now.

arXiv comment17 pages, 4 figures and 8 tables

References in corpus (26)

• Adam: A Method for Stochastic Optimization
• The anti-k_t jet clustering algorithm
• FastJet user manual
• An Introduction to Variational Autoencoders
• Tutorial on Variational Autoencoders
• NICE: Non-linear Independent Components Estimation
• Normalizing Flows: An Introduction and Review of Current Methods
• Particle-flow reconstruction and global event description with the CMS detector
• Towards Jetography
• ParticleNet: Jet Tagging via Particle Clouds
• CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
• Jet Substructure at the Large Hadron Collider: Experimental Review
• Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
• Search for new resonances in mass distributions of jet pairs using 139 fb${}^{-1}$ of $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Search for high mass dijet resonances with a new background prediction method in proton-proton collisions at $\sqrt{s}=$ 13 TeV
• Energy flow polynomials: A complete linear basis for jet substructure
• JEDI-net: a jet identification algorithm based on interaction networks
• DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC
• Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network
• Fast Point Cloud Generation with Diffusion Models in High Energy Physics
• Evaluating generative models in high energy physics
• EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets
• PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics
• CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds
• Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders
• Variational Autoencoders with Normalizing Flow Decoders