Benchmarking deep learning for automated peak detection on GIWAXS data

dc.contributor.author	Starostin, Vladimir
dc.contributor.author	Lapkin, Dmitry
dc.contributor.author	Munteanu, Valentin
dc.contributor.author	Romodin, Mikhail
dc.contributor.author	Hylinski, Maik
dc.contributor.author	Gerlach, Alexander
dc.contributor.author	Hinderhofer, Alexander
dc.contributor.author	Schreiber, Frank
dc.contributor.author	Völter, Constantin
dc.date.accessioned	2026-10-27T10:38:22Z
dc.date.available	2026-10-27T10:38:22Z
dc.date.issued	2025
dc.identifier.issn	1600-5767
dc.identifier.uri	http://hdl.handle.net/10900/185030
dc.language.iso	en	de_DE
dc.publisher	Chester : Int Union Crystallography	de_DE
dc.relation.uri	http://dx.doi.org/10.1107/S1600576725000974	de_DE
dc.subject.ddc	540	de_DE
dc.subject.ddc	570	de_DE
dc.subject.ddc	610	de_DE
dc.title	Benchmarking deep learning for automated peak detection on GIWAXS data	de_DE
dc.type	Article	de_DE
utue.quellen.id	20250630000000_01103
utue.publikation.seiten	513-522	de_DE
utue.personen.roh	Voelter, Constantin
utue.personen.roh	Starostin, Vladimir
utue.personen.roh	Lapkin, Dmitry
utue.personen.roh	Munteanu, Valentin
utue.personen.roh	Romodin, Mikhail
utue.personen.roh	Hylinski, Maik
utue.personen.roh	Gerlach, Alexander
utue.personen.roh	Hinderhofer, Alexander
utue.personen.roh	Schreiber, Frank
dcterms.isPartOf.ZSTitelID	Journal of Applied Crystallography	de_DE
dcterms.isPartOf.ZS-Volume	58	de_DE
utue.fakultaet	07 Mathematisch-Naturwissenschaftliche Fakultät	de_DE