Application of machine learning for energy reconstruction in the ATLAS liquid argon calorimeter

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dc.contributor.authorPolson, Lucas A.
dc.contributor.supervisorLefebvre, Michel
dc.date.accessioned2021-07-07T02:03:28Z
dc.date.available2021-07-07T02:03:28Z
dc.date.copyright2021en_US
dc.date.issued2021-07-06
dc.degree.departmentDepartment of Physics and Astronomyen_US
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractThe beam intensity of the Large Hadron Collider will be significantly increased during the Phase-II long shut down of 2024-2026. Signal processing techniques that are used to extract the energy of detected particles in ATLAS will suffer a significant loss in performance under these conditions. This study compares the presently used optimal filter technique to alternative machine learning algorithms for signal processing. The machine learning algorithms are shown to outperform the optimal filter in many relevant metrics for energy extraction. This thesis also explores the implementation of machine learning algorithms on ATLAS hardware.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/13097
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectphysicsen_US
dc.subjectmachine learningen_US
dc.subjectHEPen_US
dc.subjecthigh energy physicsen_US
dc.subjectmathen_US
dc.subjectmathematicsen_US
dc.subjectparticle physicsen_US
dc.subjectexperimental physicsen_US
dc.subjectexperimentalen_US
dc.subjectartificial intelligenceen_US
dc.subjectsignal processingen_US
dc.subjecthighen_US
dc.subjectenergyen_US
dc.subjectparticleen_US
dc.subjectCERNen_US
dc.subjectLHCen_US
dc.subjectlarge hadron collideren_US
dc.subjectATLASen_US
dc.subjectatlasen_US
dc.subjectatlas detectoren_US
dc.subjectdetectoren_US
dc.titleApplication of machine learning for energy reconstruction in the ATLAS liquid argon calorimeteren_US
dc.typeThesisen_US

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