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Pregled bibliografske jedinice broj: 1095261

Separation of Quark Flavors Using Deeply Virtual Compton Scattering Data


Čuić, Marija; Kumerički, Krešimir; Schäfer, Andreas
Separation of Quark Flavors Using Deeply Virtual Compton Scattering Data // Physical review letters, 125 (2020), 23; 232005, 5 doi:10.1103/physrevlett.125.232005 (međunarodna recenzija, članak, znanstveni)


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Naslov
Separation of Quark Flavors Using Deeply Virtual Compton Scattering Data

Autori
Čuić, Marija ; Kumerički, Krešimir ; Schäfer, Andreas

Izvornik
Physical review letters (0031-9007) 125 (2020), 23; 232005, 5

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Hadron Structure, Quarks, Generalized Parton Distributions, Neural Networks

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Fizika



POVEZANOST RADA


Projekti:
HRZZ-IP-2019-04-9709 - Razotkrivanje strukture hadrona pomoću tvrdih ekskluzivnih procesa (REVESTRUCTURE) (Passek-Kumerički, Kornelija, HRZZ - 2019-04) ( POIROT)
EK-H2020-824093 - The strong interaction at the frontier of knowledge: fundamental research and applications (STRONG-2020) (Korolija, Milorad; Bosnar, Damir; Kumerički, Krešimir; Passek-Kumerički, Kornelija, EK - H2020-INFRAIA-2018-1) ( POIROT)

Ustanove:
Prirodoslovno-matematički fakultet, Zagreb

Profili:

Avatar Url Krešimir Kumerički (autor)

Avatar Url Marija Čuić (autor)

Poveznice na cjeloviti tekst rada:

doi journals.aps.org

Citiraj ovu publikaciju:

Čuić, Marija; Kumerički, Krešimir; Schäfer, Andreas
Separation of Quark Flavors Using Deeply Virtual Compton Scattering Data // Physical review letters, 125 (2020), 23; 232005, 5 doi:10.1103/physrevlett.125.232005 (međunarodna recenzija, članak, znanstveni)
Čuić, M., Kumerički, K. & Schäfer, A. (2020) Separation of Quark Flavors Using Deeply Virtual Compton Scattering Data. Physical review letters, 125 (23), 232005, 5 doi:10.1103/physrevlett.125.232005.
@article{article, author = {\v{C}ui\'{c}, Marija and Kumeri\v{c}ki, Kre\v{s}imir and Sch\"{a}fer, Andreas}, year = {2020}, pages = {5}, DOI = {10.1103/physrevlett.125.232005}, chapter = {232005}, keywords = {Hadron Structure, Quarks, Generalized Parton Distributions, Neural Networks}, journal = {Physical review letters}, doi = {10.1103/physrevlett.125.232005}, volume = {125}, number = {23}, issn = {0031-9007}, title = {Separation of Quark Flavors Using Deeply Virtual Compton Scattering Data}, keyword = {Hadron Structure, Quarks, Generalized Parton Distributions, Neural Networks}, chapternumber = {232005} }
@article{article, author = {\v{C}ui\'{c}, Marija and Kumeri\v{c}ki, Kre\v{s}imir and Sch\"{a}fer, Andreas}, year = {2020}, pages = {5}, DOI = {10.1103/physrevlett.125.232005}, chapter = {232005}, keywords = {Hadron Structure, Quarks, Generalized Parton Distributions, Neural Networks}, journal = {Physical review letters}, doi = {10.1103/physrevlett.125.232005}, volume = {125}, number = {23}, issn = {0031-9007}, title = {Separation of Quark Flavors Using Deeply Virtual Compton Scattering Data}, keyword = {Hadron Structure, Quarks, Generalized Parton Distributions, Neural Networks}, chapternumber = {232005} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus
  • MEDLINE
  • Nature Index


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