prikaz prve stranice dokumenta Separation of Quark Flavors Using Deeply Virtual Compton Scattering Data
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Scientific paper - Original scientific paper
Separation of Quark Flavors Using Deeply Virtual Compton Scattering Data
Physical review letters, 125 (2020), 23; 232005. https://doi.org/10.1103/physrevlett.125.232005
Čuić, Marija; Kumerički, Krešimir; Schäfer, Andreas

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Čuić, M., Kumerički, K. & Schäfer, A. (2020). Separation of Quark Flavors Using Deeply Virtual Compton Scattering Data. Physical review letters, 125. (23). doi: 10.1103/physrevlett.125.232005

Čuić, Marija, et al. "Separation of Quark Flavors Using Deeply Virtual Compton Scattering Data." Physical review letters, vol. 125, no. 23, 2020. https://doi.org/10.1103/physrevlett.125.232005

Čuić, Marija, Krešimir Kumerički and Andreas Schäfer. "Separation of Quark Flavors Using Deeply Virtual Compton Scattering Data." Physical review letters 125, no. 23 (2020). https://doi.org/10.1103/physrevlett.125.232005

Čuić, M., Kumerički, K. and Schäfer, A. (2020) 'Separation of Quark Flavors Using Deeply Virtual Compton Scattering Data', Physical review letters, 125(23). doi: 10.1103/physrevlett.125.232005

Čuić M, Kumerički K, Schäfer A. Separation of Quark Flavors Using Deeply Virtual Compton Scattering Data. Physical review letters [Internet]. 2020 [cited 2024 April 27];125(23). doi: 10.1103/physrevlett.125.232005

M. Čuić, K. Kumerički and A. Schäfer, "Separation of Quark Flavors Using Deeply Virtual Compton Scattering Data", Physical review letters, vol. 125, no. 23, 2020. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:217:398599. [Accessed: 27 April 2024]

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Metadata
Title (english)Separation of Quark Flavors Using Deeply Virtual Compton Scattering Data
AuthorMarija Čuić
AuthorKrešimir Kumerički
AuthorAndreas Schäfer
Author's institutionUniversity of Zagreb
Faculty of Science
(Department of Physics)
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Physics
Abstract (english)
Using the available data on deeply virtual Compton scattering (DVCS) off protons and utilizing neural networks enhanced by the dispersion relation constraint, we determine six out of eight leading Compton form factors in the valence quark kinematic region. Furthermore, adding recent data on DVCS off neutrons, we separate contributions of up and down quarks to the dominant form factor, thus paving the way towards a three-dimensional picture of the nucleon.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titlePhysical review letters
Numberingvol. 125, no. 23, 232005
p-ISSN0031-9007
DOIhttps://doi.org/10.1103/physrevlett.125.232005
URN:NBNurn:nbn:hr:217:398599
Publication2020
ProjectNumber: IP-2019-04-9709
Title (croatian): Razotkrivanje strukture hadrona pomoću tvrdih ekskluzivnih procesa
Title (english): Revealing the hadron structure through hard exclusive processes
Acronym: REVESTRUCTURE
Leader: Kornelija Passek-Kumerički
Jurisdiction: Croatia
Funding stream: IP
Document URLhttp://bib.irb.hr/1095261
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Public notePublished by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3. Received 8 July 2020; revised 14 October 2020; accepted 10 November 2020; published 2 December 2020.
Created on2023-04-06 14:29:58