Year of Graduation
Development of Track Recognition Algorithm for Neutrino Detector of SHIP Experiment
The conducted research was dedicated to the development of machine learning methods for energy and position prediction for the physics tracks inside neutrino detector. The obtained results will be further used for optimization of the SHiP experiment and reduction of the costs without loss of efficiency. This research is relevant in context of physics hunting for hypothetical Dark Matter particles, which have not been discovered yet.