000626028 001__ 626028
000626028 005__ 20250822212419.0
000626028 0247_ $$2datacite_doi$$a10.3204/PUBDB-2025-01265
000626028 037__ $$aPUBDB-2025-01265
000626028 041__ $$aEnglish
000626028 1001_ $$0P:(DE-H253)PIP1105645$$aBliewert, Bryan$$b0$$eCorresponding author$$gmale$$udesy
000626028 245__ $$aImplementation of the Matrix Element Method and a Jet Clustering Algorithm with Machine Learning at Future Higgs Factories$$f2023-04-03 - 2024-04-04
000626028 260__ $$c2025
000626028 300__ $$a104
000626028 3367_ $$2DataCite$$aOutput Types/Supervised Student Publication
000626028 3367_ $$02$$2EndNote$$aThesis
000626028 3367_ $$2BibTeX$$aMASTERSTHESIS
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000626028 3367_ $$0PUB:(DE-HGF)19$$2PUB:(DE-HGF)$$aMaster Thesis$$bmaster$$mmaster$$s1744291613_3645138
000626028 3367_ $$2ORCID$$aSUPERVISED_STUDENT_PUBLICATION
000626028 502__ $$aMasterarbeit, Technische Universität München, 2024$$bMasterarbeit$$cTechnische Universität München$$d2024
000626028 520__ $$aA top priority of future collider programs is to measure the value of the Higgs self-coupling λ. Through double Higgs production (ZHH), this is possible by direct measurement at lepton col-liders. However, both reconstruction and analysis face challenges due to the high number of jets, misclustering effects in the jet clustering procedure and separation of the signal from irreducible backgrounds (ZZH). In this thesis, approaches and solutions for both are presented. First, a jet clustering algorithm based on Graph Neural Networks and Spectral Clustering is presented and shown to produce nearly identical as the benchmark (Durham algorithm). Then, for the analysis, multiple multivariate methods are explored, such as likelihood-ratio testing with the Matrix-Element-Method and direct classification using machine learning models including transformers and Deep Sets. The best results give a final average precision and AUROC for separating ZHH and ZZH events correctly of 67% and 0.78, respectively.
000626028 536__ $$0G:(DE-HGF)POF4-611$$a611 - Fundamental Particles and Forces (POF4-611)$$cPOF4-611$$fPOF IV$$x0
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000626028 7001_ $$0P:(DE-H253)PIP1005630$$aList, Jenny$$b1$$eThesis advisor$$udesy
000626028 7001_ $$0P:(DE-HGF)0$$aHeinrich, Lukas$$b2$$eThesis advisor
000626028 7001_ $$0P:(DE-H253)PIP1081743$$aKasieczka, Gregor$$b3$$eThesis advisor
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000626028 9101_ $$0I:(DE-588b)2008985-5$$6P:(DE-H253)PIP1105645$$aDeutsches Elektronen-Synchrotron$$b0$$kDESY
000626028 9101_ $$0I:(DE-588b)36241-4$$6P:(DE-H253)PIP1105645$$aTechnische Universität München$$b0$$kTUM
000626028 9101_ $$0I:(DE-588b)2008985-5$$6P:(DE-H253)PIP1005630$$aDeutsches Elektronen-Synchrotron$$b1$$kDESY
000626028 9101_ $$0I:(DE-588b)36241-4$$6P:(DE-HGF)0$$aTechnische Universität München$$b2$$kTUM
000626028 9101_ $$0I:(DE-HGF)0$$6P:(DE-H253)PIP1081743$$a Universität Hamburg$$b3
000626028 9131_ $$0G:(DE-HGF)POF4-611$$1G:(DE-HGF)POF4-610$$2G:(DE-HGF)POF4-600$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bForschungsbereich Materie$$lMatter and the Universe$$vFundamental Particles and Forces$$x0
000626028 9141_ $$y2025
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000626028 9201_ $$0I:(DE-H253)FTX-20210408$$kFTX$$lTechnol. zukünft. Teilchenph. Experim.$$x0
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