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

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.doi	http://doi.org/10.25358/openscience-5122
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/5126
dc.identifier.urn	urn:nbn:de:hebis:77-openscience-457f07ef-20c4-406b-b1ce-bed889b56a082
dc.language.iso	eng	de
dc.rights	CC-BY-NC-ND-4.0	*
dc.rights.uri	https://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
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.name	Johannes Gutenberg-Universität Mainz
jgu.organisation.number	7940
jgu.organisation.place	Mainz
jgu.organisation.ror	https://ror.org/023b0x485
jgu.rights.accessrights	openAccess
jgu.subject.ddccode	500	de
jgu.subject.ddccode	530	de
jgu.type.dinitype	PhDThesis	en_GB
jgu.type.resource	Text	de
jgu.type.version	Original work	de

Files

Now showing 1 - 1 of 1

Now showing 1 - 1 of 1