Development of a Machine Learning Algorithm for the Searches of the New Вс+ Meson Decays with Charmonium and Multihadron Final States

The paper describes the process of implementation of machine learning algorithm for the classification of the events in high energy physics. The results of testing a classifier based on gradient boosted decision tree to improve the selection efficiency of the rare Bc meson decays with charmonium and multihadron final states are presented. The development of the algorithm is performed using a toolkit for multivariate data analysis. The training of the classifier is based on the simulated data and experimental data, collected by the LHCb detector at the Large Hadron Collider in the period from 2011 to 2018.

Keywords

multivariate analysis; machine learning; data analysis; decision tree; beauty hadrons; charmonium.

References

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