Применение генеративных моделей для физических экспериментовGenerative Models for High Energy Physics Experiments

The Large Hadron Collider (LHC), which was built by the European Organization for Nuclear Research (CERN), is the world’s largest collider. The LHCb experiment at the LHC focuses on studying heavy flavor physics, making precise measurements of CP violation, and investigating other effects within and beyond the Standard Model.The LHCb detector consists of several components, including an electromagnetic calorimeter (ECAL). Simulating the expected detector response is crucial for the physical analysis of the collected data and deriving physical results. Using theGeant4 package to simulate detector responses is computationally expensive and resource intensive, so there is a need to speed up this process. This research paper explores the potential of using generative adversarial networks (GANs) to accelerate the simulation process of calorimeter response in high-energy physics experiments. The thesis contribution consists of three essential parts. The model’s performance is highly sensitive to its architecture, so it was improved comparing to the previously published once. The second contribution is a method that helps the model to take particular properties of generated objects into account and improve its generation quality. This method requires increasing models capacity, so an other approach that allows to balance between training stability and expressivity is proposed. By proposing methods to enhance simulation speed and improve calorimeter response accuracy, this work holds significant implications for the advancement ofLHCb and other high-energy physics projects.