The current work deals with the reconstruction of particle trajectory parameters in the BM@N experiment by machine learning methods

Tasks

The participant is invited to:
-learn to work in the BmnRoot framework, to model and reconstruct events using standard methods
-develop an algorithm for searching particle trajectories using machine learning methods

Preliminary schedule by topics/tasks

First 2 weeks:
-installing, configuring and learning the basics of BmnRoot
-learning the reconstructed BM@N high-level data format

Remaining 5 weeks:
-developing the architecture of the ML algorithm for track reconstruction
-writing programs to extract the learning parameters of the ML algorithm

Required skills

work in Linux, good C++ and STL, English, ML

Acquired skills and experience

The student will learn how to work with experimental and simulated data in high-energy physics, become familiar with working in a collaboration, and take the first steps in advanced algorithms of track reconstruction in HEP.

Recommended literature

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