Fully nonperturbative charm-quark tuning using machine learning

dc.contributor.authorHudspith, Renwick J.
dc.contributor.authorMohler, Daniel
dc.date.accessioned2023-05-03T13:29:20Z
dc.date.available2023-05-03T13:29:20Z
dc.date.issued2022
dc.description.abstractWe present a relativistic heavy-quark action tuning for the charm sector on ensembles generated by the Coordinated Lattice Simulations consortium. We tune a particular five-parameter action in an entirely nonperturbative and—up to the chosen experimental input—model-independent way using machine learning and the continuum experimental charmonium ground-state masses with various quantum numbers. In the end, we are reasonably successful; obtaining a set of simulation parameters that we then verify produces the expected spectrum. In the future, we will use this action for finite-volume calculations of hadron-hadron scattering.en_GB
dc.identifier.doihttp://doi.org/10.25358/openscience-8891
dc.identifier.urihttps://openscience.ub.uni-mainz.de/handle/20.500.12030/8907
dc.language.isoeng
dc.rightsCC-BY-4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc530 Physikde_DE
dc.subject.ddc530 Physicsen_GB
dc.titleFully nonperturbative charm-quark tuning using machine learningen_GB
dc.typeZeitschriftenaufsatzde_DE
jgu.apc.membershipSCOAP3 (SCOAP)
jgu.dfg.year2022
jgu.journal.titlePhysical review : D
jgu.journal.volume106
jgu.organisation.departmentFB 08 Physik, Mathematik u. Informatikde_DE
jgu.organisation.nameJohannes Gutenberg-Universität Mainzde_DE
jgu.organisation.number7940
jgu.organisation.placeMainz
jgu.organisation.rorhttps://ror.org/023b0x485
jgu.pages.alternative034508
jgu.publisher.doi10.1103/physrevd.106.034508
jgu.publisher.issn2470-0029
jgu.publisher.nameAmerican Institute of Physics
jgu.publisher.placeMelville, NY
jgu.publisher.year2022
jgu.rights.accessrightsopenAccessen_GB
jgu.subject.ddccode530
jgu.subject.dfgNaturwissenschaftende_DE
jgu.type.contenttypeScientific articleen_GB
jgu.type.dinitypeArticleen_GB
jgu.type.resourceTexten_GB
jgu.type.versionPublished versionen_GB

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