Investigations of calorimeter clustering in ATLAS using machine learning

dc.contributor.authorNiedermayer, Graeme
dc.contributor.supervisorKowalewski, Robert V.
dc.date.accessioned2018-01-11T15:55:43Z
dc.date.available2018-01-11T15:55:43Z
dc.date.copyright2017en_US
dc.date.issued2018-01-11
dc.degree.departmentDepartment of Physics and Astronomyen_US
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractThe Large Hadron Collider (LHC) at CERN is designed to search for new physics by colliding protons with a center-of-mass energy of 13 TeV. The ATLAS detector is a multipurpose particle detector built to record these proton-proton collisions. In order to improve sensitivity to new physics at the LHC, luminosity increases are planned for 2018 and beyond. With this greater luminosity comes an increase in the number of simultaneous proton-proton collisions per bunch crossing (pile-up). This extra pile-up has adverse effects on algorithms for clustering the ATLAS detector's calorimeter cells. These adverse effects stem from overlapping energy deposits originating from distinct particles and could lead to difficulties in accurately reconstructing events. Machine learning algorithms provide a new tool that has potential to improve clustering performance. Recent developments in computer science have given rise to new set of machine learning algorithms that, in many circumstances, out-perform more conventional algorithms. One of these algorithms, convolutional neural networks, has been shown to have impressive performance when identifying objects in 2d or 3d arrays. This thesis will develop a convolutional neural network model for calorimeter cell clustering and compare it to the standard ATLAS clustering algorithm.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/8970
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectMachine Learningen_US
dc.subjectArtificial Neural Networken_US
dc.subjectConvolutional Neural Networken_US
dc.subjectCalorimetryen_US
dc.subjectParticle Physicsen_US
dc.subjectATLASen_US
dc.subjectLHCen_US
dc.subjectANNen_US
dc.subjectCNNen_US
dc.subjectTopological Clusteringen_US
dc.subjectParticle Detectoren_US
dc.subjectCERNen_US
dc.subjectEnergy Depositionsen_US
dc.subjectPile-Upen_US
dc.subjectHigh Luminosityen_US
dc.subjectResidual Neural Networken_US
dc.subjectLarge Hadron Collideren_US
dc.subjectHL-LHCen_US
dc.titleInvestigations of calorimeter clustering in ATLAS using machine learningen_US
dc.typeThesisen_US

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