Publication:
A Study of Excited Ωb- States in Hypercentral Constituent Quark Model via Artificial Neural Network

dc.authorscopusid56027387100
dc.authorwosidMutuk, Halil/Hci-7113-2022
dc.contributor.authorMutuk, Halil
dc.date.accessioned2020-06-21T09:05:01Z
dc.date.available2020-06-21T09:05:01Z
dc.date.issued2020
dc.departmentOndokuz Mayıs Üniversitesien_US
dc.department-temp[Mutuk, Halil] Ondokuz Mayis Univ, Fac Arts & Sci, Dept Phys, TR-55139 Samsun, Turkeyen_US
dc.description.abstractIn this work, we have obtained mass spectra, radiative decay widths and strong decay widths of newly observed excited Omega(-)(b) states, i.e. Omega(b)(6316)-, Omega(b)(6330)-, Omega(b)(6340)-, and Omega(b)(6350)-. Mass spectrum is obtained in Hypercentral Constituent Quark Model (hCQM) by solving six-dimensional nonrelativistic Schrodinger equation via Artificila Neural Network (ANN). In this respect, radiative decay widhts are calculated by a generalization of a framework from meson to baryon. Also, strong decay widths of the low-lying Omega(b) states within 3P0 model are calculated. Obtained results are presented with the comparison of available experimental data and other theoretical studies.en_US
dc.description.woscitationindexScience Citation Index Expanded
dc.identifier.doi10.1140/epja/s10050-020-00161-5
dc.identifier.issn1434-6001
dc.identifier.issn1434-601X
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85085304495
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1140/epja/s10050-020-00161-5
dc.identifier.volume56en_US
dc.identifier.wosWOS:000541855500001
dc.identifier.wosqualityQ2
dc.institutionauthorMutuk, Halil
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofEuropean Physical Journal Aen_US
dc.relation.journalEuropean Physical Journal Aen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleA Study of Excited Ωb- States in Hypercentral Constituent Quark Model via Artificial Neural Networken_US
dc.typeArticleen_US
dspace.entity.typePublication

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