Machine learning techniques for calorimeter cluster calibration of the CMS particle flow algorithm.

The Electromagnetic Calorimeter (ECAL) and Hadronic Calorimeter (HCAL) are key components of the CMS detector. The ECAL is designed to measure the energies of electrons and photons, while the HCAL measures the energies of charged and neutral hadrons. The Particle Flow (PF) algorithm integrates information from various CMS sub-detectors to reconstruct and identify all particles produced in proton collisions. Photons and neural hadrons are reconstructed using PF element energy clusters. A proper calibration enhances particle identification and reduces the likelihood of misreconstructed energy excess. Machine learning techniques, such as Boosted Decision Trees (BDT) and Graph Neural Networks (GNN), are employed to calibrate PF energy clusters, improving both the response and the resolution of the measured energy. This thesis applies BDT to calibrate PF ECAL clusters, while GNN is tested for hadronic cluster calibration.

Author dc:creator

• Ghazwani, Noorah Asaad, 1992-

Contributors dc:contributor

• Hatakeyama, Kenichi.

• Machine learning.
• Experimental particle physics.
• Compact muon solenoid (CMS)
• Calibration.

Statement dc:rights

• Worldwide access
• Baylor University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact libraryquestions@baylor.edu for inquiries about permission.

Language dc:language

en

Handle dc:identifier.uri

https://hdl.handle.net/2104/13857

OAI identifier oai:identifier

oai:tdl-ir.tdl.org:2104/13857