  We introduce a novel variational autoencoder (VAE) architecture that can
generate realistic and diverse high energy physics events. The model we propose
utilizes several techniques from VAE literature in order to simulate high
fidelity jet images. In addition to demonstrating the model's ability to
produce high fidelity jet images through various assessments, we also
demonstrate its ability to control the events it generates from the latent
space. This can be potentially useful for other tasks such as jet tagging,
where we can test how well jet taggers can classify signal from background for
events generated by the VAE. We test this idea by seeing the signal efficiency
vs background rejection for different types of jet images produced by our
model. We compare our VAE with generative adversarial networks (GAN) in several
ways, most notably in speed. The architecture we propose is ultimately a fast,
stable, and easy-to-train deep generative model that demonstrates the potential
of VAEs in simulating high energy physics events.
