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Advanced Machine-Learning Techniques for Improving Jet Tagging in Particle Physics: An Analysis of LHC Data Using Unsupervised Clustering and Particle Representation Methods

CMS Jet event

This repository contains the code and materials for a research project on developing and testing advanced machine-learning algorithms for jet tagging in particle physics. The project focuses on identifying the particles that generated the jets, including W and Z bosons, $\tau$ leptons, and b quarks, as well as the QCD background. The research is based on recent jet tagging techniques, such as unsupervised clustering (UCluster) and a novel representation of jets as an unordered set of particles (ParticleNet).

The project uses data from the Large Hadron Collider (LHC) from the CMS experiment, which is available through the CMS Open Data Portal.

References

The project is based on the following papers:

  1. Cagnotta, A.; Carnevali, F.; De Iorio, A. Machine Learning Applications for Jet Tagging in the CMS Experiment. Appl. Sci. 2022, 12, 10574. https://doi.org/10.3390/app122010574.
  2. Vinicius Mikuni and Florencia Canelli. Unsupervised clustering for collider physics. Phys. Rev. D, 103:092007, May 2021. URL: https://link.aps.org/doi/10.1103/PhysRevD.103.092007, doi:10.1103/PhysRevD.103.092007.
  3. Huilin Qu and Loukas Gouskos. Jet tagging via particle clouds. Phys. Rev. D, 101:056019, Mar 2020. URL: https://link.aps.org/doi/10.1103/PhysRevD.101.056019, doi:10.1103/PhysRevD.101.056019.
  4. Eric M. Metodiev, Benjamin Nachman, and Jesse Thaler. Classification without labels: learning from mixed samples in high energy physics. Journal of High Energy Physics, 2017(10):174, Oct 2017. doi:10.1007/JHEP10(2017)174.
  5. Jack H. Collins, Kiel Howe, and Benjamin Nachman. Extending the search for new resonances with machine learning. Phys. Rev. D, 99:014038, Jan 2019. URL: https://link.aps.org/doi/10.1103/PhysRevD.99.014038, doi:10.1103/PhysRevD.99.014038.

Authors

The project is being developed by