Application of the Hopfield Network to SPD Track Reconstruction We offer to your attention a preprint «Application of the Hopfield Network to SPD Track Reconstruction» (P11-2024-5) issued by the JINR Publishing Department. The authors: Bureš M., Kadochnikov I.S., Kovalenko A.V., Ososkov G.A. One of the key stages of processing data from particle physics experiments is the reconstruction of trajectories (tracks) of interacting particles from measurement data. In the SPD experiment planned at the NICA collider, a special difficulty will be caused by the extremely high frequency of interactions (3 MHz), which leads to overlapping of events during the data acquisition in the time-slice mode, as well as by the strong contamination of data by fake measurements due to the specifics of the track detectors. This makes track reconstruction (tracking) algorithms very complicated. In this study, methods based on the Hopfield neural network for tracking simulated events of the SPD experiment are investigated. Taking into account the specifics of the experiment, optimization of the energy function parameters of the neural network is proposed to improve the tracking results, and the applicability of quantum annealing to the SPD tracking problem is investigated The investigation has been performed at the Meshcheryakov Laboratory of Information Technologies, JINR.
