  The research of innovative methods aimed at reducing costs and shortening the
time needed for simulation, going beyond conventional approaches based on Monte
Carlo methods, has been sparked by the development of collision simulations at
the Large Hadron Collider at CERN. Deep learning generative methods including
VAE, GANs and diffusion models have been used for this purpose. Although they
are much faster and simpler than standard approaches, they do not always keep
high fidelity of the simulated data. This work aims to mitigate this issue, by
providing an alternative solution to currently employed algorithms by
introducing the mechanism of control over the generated data properties. To
achieve this, we extend the recently introduced CorrVAE, which enables
user-defined parameter manipulation of the generated output. We adapt the model
to the problem of particle physics simulation. The proposed solution achieved
promising results, demonstrating control over the parameters of the generated
output and constituting an alternative for simulating the ZDC calorimeter in
the ALICE experiment at CERN.
