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Investigations of calorimeter clustering in ATLAS using machine learning

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dc.contributor.author Niedermayer, Graeme
dc.date.accessioned 2018-01-11T15:55:43Z
dc.date.available 2018-01-11T15:55:43Z
dc.date.copyright 2017 en_US
dc.date.issued 2018-01-11
dc.identifier.uri https://dspace.library.uvic.ca//handle/1828/8970
dc.description.abstract The 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.language English eng
dc.language.iso en en_US
dc.rights Available to the World Wide Web en_US
dc.subject Machine Learning en_US
dc.subject Artificial Neural Network en_US
dc.subject Convolutional Neural Network en_US
dc.subject Calorimetry en_US
dc.subject Particle Physics en_US
dc.subject ATLAS en_US
dc.subject LHC en_US
dc.subject ANN en_US
dc.subject CNN en_US
dc.subject Topological Clustering en_US
dc.subject Particle Detector en_US
dc.subject CERN en_US
dc.subject Energy Depositions en_US
dc.subject Pile-Up en_US
dc.subject High Luminosity en_US
dc.subject Residual Neural Network en_US
dc.subject Large Hadron Collider en_US
dc.subject HL-LHC en_US
dc.title Investigations of calorimeter clustering in ATLAS using machine learning en_US
dc.type Thesis en_US
dc.contributor.supervisor Kowalewski, Robert V.
dc.degree.department Department of Physics and Astronomy en_US
dc.degree.level Master of Science M.Sc. en_US
dc.description.scholarlevel Graduate en_US


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