Seminar

One of the key stages in the processing of experimental data from HEP is the reconstruction of trajectories (tracks) of interacting particles from measurement data. In the SPD experiment planned at the NICA collider, a major difficulty will be caused by the extremely high frequency of interactions (3 MHz) due to the high luminosity of the particle beams, leading to the overlap of events during their acquisition in the time-slice mode, as well as by a strong contamination of data by false measurements due to the peculiarities of the SPD track detectors.
In this talk we present the current status of works on the development of an efficient tracking algorithm for model events of the SPD experiment based on the application of the Hopfield neural network. Taking into account the specifics of the experiment, an optimization of the parameters of the neural network energy function is proposed to improve the tracking results.
In order to substantially accelerate the SPD tracking procedure, the applicability of quantum algorithms for this purpose is investigated. In this setting, the tracking problem is formulated as quadratic unconstrained binary optimization (QUBO) and solved by simulated annealing or quantum annealing. Successful results from recent work on solving combinatorial optimization problems by quantum methods point to their possible applications for fast data processing of SPD or other high-luminosity experiments. A pilot study of the method on TrackML data has been performed, and we intend to adapt this approach to SPD model data.

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Information on the seminar and the link to connect are available at Indico.