000607309 001__ 607309
000607309 005__ 20250407222508.0
000607309 0247_ $$2datacite_doi$$a10.3204/PUBDB-2024-01825
000607309 037__ $$aPUBDB-2024-01825
000607309 041__ $$aEnglish
000607309 088__ $$2DESY$$aDESY-THESIS-2024-008
000607309 1001_ $$0P:(DE-H253)PIP1030902$$aMcKeown, Peter$$b0$$eCorresponding author$$gmale$$udesy
000607309 245__ $$aDevelopment and Performance of a Fast Simulation Tool for Showers in High Granularity Calorimeters based on Deep Generative Models$$f2020-10-01 - 2024-02-29
000607309 260__ $$aHamburg$$bVerlag Deutsches Elektronen-Synchrotron DESY$$c2024
000607309 300__ $$a186
000607309 3367_ $$2DataCite$$aOutput Types/Dissertation
000607309 3367_ $$0PUB:(DE-HGF)3$$2PUB:(DE-HGF)$$aBook$$mbook
000607309 3367_ $$2ORCID$$aDISSERTATION
000607309 3367_ $$2BibTeX$$aPHDTHESIS
000607309 3367_ $$02$$2EndNote$$aThesis
000607309 3367_ $$0PUB:(DE-HGF)11$$2PUB:(DE-HGF)$$aDissertation / PhD Thesis$$bphd$$mphd$$s1744016235_1668336
000607309 3367_ $$2DRIVER$$adoctoralThesis
000607309 4900_ $$aDESY-THESIS
000607309 502__ $$aDissertation, Universität Hamburg, 2024$$bDissertation$$cUniversität Hamburg$$d2024
000607309 520__ $$aModern high energy physics experiments fundamentally rely on large quantities of simulateddata, placing significant demands on the available computational resources. Machine learningmethods based on deep generative models promise to reduce the compute time required tosimulate particle showers in the calorimeter system, which constitutes the most computationallyintensive part of a full detector simulation.This work focuses on the development of a first simulation tool based on deep generativemodels for shower simulation in highly granular calorimeters, and subsequently studies itsperformance in a realistic detector geometry. In order to apply these models in a generalsimulation, they must provide a suitable detector response for particles incident under variousangles to, and at various positions in, the detector. Crucially, the physics performance afterreconstruction must remain high, which is the ultimate target of such a simulator.We initially extend the performant Bounded Information Bottleneck Autoencoder (BIB-AE) to simulate showers from photons with varying incident energy and angle to the surface ofthe electromagnetic calorimeter of the International Large Detector (ILD), before studying thesingle particle performance of the model in terms of key calorimetric observables, both beforeand after reconstruction. We then further extend the model to handle an additional angle ofincidence, as well as taking steps to deal with geometry irregularities in order to allow the useof the model at different positions in the calorimeter.As a next step, we describe a generic library that enables the use of generative models withGeant4 and DD4hep, allowing a full integration into standard software ecosystems used inhigh energy physics. We outline the integration of the BIB-AE into this library, allowing afair benchmark of the computational performance of the model. We then simulate showers atdifferent positions with the model, in order to investigate the effects of performing simulationsin an irregular calorimeter geometry.Finally, we study the performance of the BIB-AE when used to simulate photons fromneutral pion decays in the process $e^{+}e^{-}\rightarrow \tau^{+}\tau^{-}$ in terms of key physics observables. We findthat while some deviations from Geant4 occur, they are typically comparable to the MonteCarlo uncertainty, estimated from the performance differences between Geant4 versions.
000607309 536__ $$0G:(DE-HGF)POF4-623$$a623 - Data Management and Analysis (POF4-623)$$cPOF4-623$$fPOF IV$$x0
000607309 536__ $$0G:(EU-Grant)101004761$$aAIDAinnova - Advancement and Innovation for Detectors at Accelerators (101004761)$$c101004761$$fH2020-INFRAINNOV-2020-2$$x1
000607309 536__ $$0G:(GEPRIS)390833306$$aDFG project G:(GEPRIS)390833306 - EXC 2121: Quantum Universe (390833306)$$c390833306$$x2
000607309 536__ $$0G:(DE-Ds200)BMBF-05D23GU4$$a05D23GU4 - Verbundprojekt 05D2022 - KISS: Künstliche Intelligenz zur schnellen Simulation von wissenschaftlichen Daten. Teilprojekt 1. (BMBF-05D23GU4)$$cBMBF-05D23GU4$$f05D23GU4$$x3
000607309 536__ $$0G:(DE-HGF)2015_IFV-VH-GS-500$$aPHGS, VH-GS-500 - PIER Helmholtz Graduate School (2015_IFV-VH-GS-500)$$c2015_IFV-VH-GS-500$$x4
000607309 693__ $$0EXP:(DE-MLZ)NOSPEC-20140101$$5EXP:(DE-MLZ)NOSPEC-20140101$$eNo specific instrument$$x0
000607309 7001_ $$0P:(DE-H253)PIP1002530$$aGaede, Frank$$b1$$eThesis advisor$$udesy
000607309 7001_ $$0P:(DE-H253)PIP1081743$$aKasieczka, Gregor$$b2$$eThesis advisor
000607309 8564_ $$uhttps://bib-pubdb1.desy.de/record/607309/files/desy-thesis-24-008.title.pdf$$yOpenAccess
000607309 8564_ $$uhttps://bib-pubdb1.desy.de/record/607309/files/mckeown_thesis.pdf$$yOpenAccess
000607309 8564_ $$uhttps://bib-pubdb1.desy.de/record/607309/files/desy-thesis-24-008.title.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000607309 8564_ $$uhttps://bib-pubdb1.desy.de/record/607309/files/mckeown_thesis.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000607309 909CO $$ooai:bib-pubdb1.desy.de:607309$$pVDB$$pdriver$$popen_access$$pdnbdelivery$$pec_fundedresources$$popenaire
000607309 9101_ $$0I:(DE-588b)2008985-5$$6P:(DE-H253)PIP1030902$$aDeutsches Elektronen-Synchrotron$$b0$$kDESY
000607309 9101_ $$0I:(DE-588b)2008985-5$$6P:(DE-H253)PIP1002530$$aDeutsches Elektronen-Synchrotron$$b1$$kDESY
000607309 9101_ $$0I:(DE-HGF)0$$6P:(DE-H253)PIP1081743$$aExternal Institute$$b2$$kExtern
000607309 9131_ $$0G:(DE-HGF)POF4-623$$1G:(DE-HGF)POF4-620$$2G:(DE-HGF)POF4-600$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bForschungsbereich Materie$$lMaterie und Technologie$$vData Management and Analysis$$x0
000607309 9141_ $$y2024
000607309 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000607309 920__ $$lyes
000607309 9201_ $$0I:(DE-H253)FTX-20210408$$kFTX$$lTechnol. zukünft. Teilchenph. Experim.$$x0
000607309 980__ $$aphd
000607309 980__ $$aVDB
000607309 980__ $$abook
000607309 980__ $$aI:(DE-H253)FTX-20210408
000607309 980__ $$aUNRESTRICTED
000607309 9801_ $$aFullTexts