Particle physics DL-simulation with control over generated data properties

TL;DR

This paper introduces an extended CorrVAE model for particle physics simulation that allows control over generated data properties, providing a faster alternative to traditional Monte Carlo methods with promising results in simulating CERN detector data.

Contribution

It extends CorrVAE to enable user-defined control over generated particle physics data, improving fidelity and flexibility in simulation tasks.

Findings

Achieved control over generated data parameters.

Demonstrated effective simulation of CERN detector data.

Provided a promising alternative to Monte Carlo methods.

Abstract

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.