Browsing by study line "Tutkimuksen opintosuunta"

The LHC particle accelerator at CERN probes the elementary building blocks of matter by colliding protons at a center-of-mass energy of √s = 13 TeV. Collimated sprays of particles arise when quarks and gluons are produced at high energies, that are reconstructed from measured data and clustered together into jets. Accurate measurements of the energy of jets are paramount for sensitive particle physics analyses at the CMS experiment. Jet energy corrections are for that reason used to map measurements towards Monte Carlo simulated truth values, which are independent of detector response. The aim of this thesis is to improve upon the standard jet energy corrections by utilizing deep learning. Recent advancements on learning from point clouds in the machine learning community have been adopted in particle physics studies to improve jet flavor classification accuracy. This includes representing jet constituents as an unordered set, or a so-called “particle cloud”. Two highly performant models suitable for such data are the set-based Particle Flow Network and the graph-based ParticleNet. A natural next step in the advancement of jet energy corrections is to adopt a similar methodology, only changing the problem statement from classification to regression. The deep learning models developed in this work provide energy corrections that are generically applicable to differently flavored jets. Their performance is presented in the form of jet energy response resolution and reduction in flavor dependence. The models achieve state of the art performance for both metrics, significantly surpassing the standard corrections benchmark.