Machine Learning Methods for Particle Identification in Data of the LHCb (CERN) Detector

Student: Korolev Sergey

Supervisor: Leonid E Zhukov

Educational Programme: Data Science (Master)

Year of Graduation: 2016

In this work, the data is explored for particle identification task on LHCb detector and results are acquired for flatness boosting using gradient boosting models. I show that adding a flatness term in loss function of the model enhances its performance for high energy physics tasks and compare a set of models both with and without flatness loss. I also describe the existing particle identification systems and their features, show the improvement that can be achieved with modern supervised learning models and discuss following research.

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