  This thesis investigates the application of state-of-the-art advances in
generative neural networks for fast simulation of the Zero Degree Calorimeter
(ZDC) neutron detector in the ALICE experiment at CERN. Traditional simulation
methods using the GEANT Monte Carlo toolkit, while accurate, are
computationally demanding. With increasing computational needs at CERN,
efficient simulation techniques are essential. The thesis provides a
comprehensive literature review on the application of neural networks in
computer vision, fast simulations using machine learning, and generative neural
networks in high-energy physics. The theory of the analyzed models is also
discussed, along with technical aspects and the challenges associated with a
practical implementation. The experiments evaluate various neural network
architectures, including convolutional neural networks, vision transformers,
and MLP-Mixers, as well as generative frameworks such as autoencoders,
generative adversarial networks, vector quantization models, and diffusion
models. Key contributions include the implementation and evaluation of these
models, a significant improvement in the Wasserstein metric compared to
existing methods with a low generation time of 5 milliseconds per sample, and
the formulation of a list of recommendations for developing models for fast ZDC
simulation. Open-source code and detailed hyperparameter settings are provided
for reproducibility. Additionally, the thesis outlines future research
directions to further enhance simulation fidelity and efficiency.
