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One of the most important aspects of data processing at SHiP [1] experiments is tracks pattern recognition. The purpose of the SHiP Spectrometer Tracker (SST) is efficient reconstruction of charged particle tracks originating from decays of neutral New Physics objects. The reconstruction performance strongly depends on the tracker design and should be considered as an objective to define the best SST geometry parameters. In this study the SHiP Spectrom eter Tracker geometry optimization using Bayesian optimization with Gaussian processes in considered. The study have been done on MC data. The first results of the optimization are also considered.

Optimization of the SHiP Spectrometer Tracker geometry using the Bayesian Optimization with Gaussian Processes