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

The Large Hadron Collider (LHC), which was built by the European Organi-zation for Nuclear Research (CERN), is the world’s largest collider. The LHCb
experiment at the LHC focuses on studying heavy flavor physics, making precisemeasurements of CP violation, and investigating other effects within and beyondthe Standard Model.The LHCb detector consists of several components, including an electromagneticcalorimeter (ECAL). Simulating the expected detector response is crucial for thephysical analysis of the collected data and deriving physical results. Using theGeant4 package to simulate detector responses is computationally expensive andresource-intensive, so there is a need to speed up this process.
This research paper explores the potential of using generative adversarial net-works (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 perfor-mance is highly sensitive to its architecture, so it was improved comparing to the
previously published once. The second contribution is a method that helps themodel 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 calorimeterresponse accuracy, this work holds significant implications for the advancement ofLHCb and other high-energy physics projects.