  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.
