Seminar

In this work, based on Monte Carlo simulation data of heavy-ion collisions, it is shown for the first time for the Time Projection Chamber (TPC) of the MPD/NICA experiment that the distribution of ionization energy loss (signal) follows a log-normal law, both at the population level and at the level of individual tracks. This observed property has a physical justification related to the multiplicative nature of fluctuation accumulation in the process of charged particle registration. Based on this observation, a new approach to particle identification is proposed, utilizing a set of statistical parameters of the track signal distribution instead of the traditional mean value. Using machine learning methods, an optimal set of track parameters is obtained, providing a 10% improvement in identification quality compared to the traditional method. The results obtained can be used to improve track reconstruction and calibration algorithms for the TPC detector in the MPD experiment.

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