Bayesian Approach to Particles Identification in the MPD Experimen
V.A. Babkin, V.M. Baryshnikov, M.G. Buryakov, A.S. Burdyko, S.G., Buzin, A.V. Dmitriev, V.I. Dronik, P.O. Dulov, A.A. Fedyunin, V.M., Golovatyuk, E.Yu. Kidanova, S.P. Lobastov, A.D. Pyatigor, M.M. Rumyantsev,, K.A. Vokhmyanina
TL;DR
This paper presents a Bayesian statistical method to optimize particle identification algorithms in the MPD experiment, improving accuracy and reducing misidentification in ion collision data analysis.
Contribution
It introduces a Bayesian approach to enhance particle identification efficiency and contamination control in the MPD experiment's TOF and TPC systems.
Findings
Optimized particle identification algorithms using Bayesian methods.
Achieved better efficiency and lower contamination rates.
Demonstrated effectiveness in MPD experimental conditions.
Abstract
Identification of particles generated by ion collisions in the NICA collider is one of the basic functions of the Multipurpose Detector (MPD). The main means of identification in MPD are the time-of-flight system (TOF) and the time-projection chamber (TPC). The article considers the optimization of the algorithms of particles identification by these systems. Under certain conditions, the use of the statistical Bayesian approach has made it possible to achieve an optimal ratio of the efficiency of particle identification and contamination by incorrectly defined particles.
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Taxonomy
TopicsCyclone Separators and Fluid Dynamics