Gutenberg Open Science: Measurement of the differential Drell-Yan production cross-section and application of deep convolutional neural networks on event images in the context of pileup mitigation

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-5122

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DC Field	Value	Language
dc.contributor.author	Brickwedde, Bernard	-
dc.date.accessioned	2020-09-10T08:57:25Z	-
dc.date.available	2020-09-10T08:57:25Z	-
dc.date.issued	2020	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/5126	-
dc.language.iso	eng	de
dc.rights	CC BY-NC-ND	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject.ddc	500 Naturwissenschaften	de_DE
dc.subject.ddc	500 Natural sciences and mathematics	en_GB
dc.subject.ddc	530 Physik	de_DE
dc.subject.ddc	530 Physics	en_GB
dc.title	Measurement of the differential Drell-Yan production cross-section and application of deep convolutional neural networks on event images in the context of pileup mitigation	en_GB
dc.type	Dissertation	de
dc.identifier.urn	urn:nbn:de:hebis:77-openscience-457f07ef-20c4-406b-b1ce-bed889b56a082	-
dc.identifier.doi	http://doi.org/10.25358/openscience-5122	-
jgu.type.dinitype	doctoralThesis	en_GB
jgu.type.version	Original work	de
jgu.type.resource	Text	de
jgu.date.accepted	2020-08-20	-
jgu.description.extent	v, 199 Seiten	de
jgu.organisation.department	FB 08 Physik, Mathematik u. Informatik	de
jgu.organisation.number	7940	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	500	de
jgu.subject.ddccode	530	de
jgu.organisation.ror	https://ror.org/023b0x485
Appears in collections:	JGU-Publikationen

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