Color glass condensate at next-to-leading order meets HERA data
Abstract
We perform the first dipole picture fit to HERA inclusive cross section data using the full next-to-leading order (NLO) impact factor combined with an improved Balitsky-Kovchegov evolution including the dominant effects beyond leading logarithmic accuracy at low x. We find that three different formulations of the evolution equation that have been proposed in the recent literature result in a very similar description of HERA data and robust predictions for future deep inelastic scattering experiments. We find evidence pointing toward a significant nonperturbative contribution to the structure function for light quarks, which stresses the need to extend the NLO impact factor calculation to massive quarks.
Main Authors
Format
Articles
Research article
Published
2020
Series
Subjects
Publication in research information system
Publisher
American Physical Society (APS)
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-202011046501Use this for linking
Review status
Peer reviewed
ISSN
2470-0010
DOI
https://doi.org/10.1103/PhysRevD.102.074028
Language
English
Published in
Physical Review D
Citation
- Beuf, G., Hänninen, H., Lappi, T., & Mäntysaari, H. (2020). Color glass condensate at next-to-leading order meets HERA data. Physical Review D, 102(7), Article 074028. https://doi.org/10.1103/PhysRevD.102.074028
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Funder(s)
Research Council of Finland
European Commission
European Commission
Research Council of Finland
Funding program(s)
Academy Project, AoF
RIA Research and Innovation Action, H2020
ERC European Research Council, H2020
Postdoctoral Researcher, AoF
Akatemiahanke, SA
RIA Research and Innovation Action, H2020
ERC European Research Council, H2020
Tutkijatohtori, SA
Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Education and Culture Executive Agency (EACEA). Neither the European Union nor EACEA can be held responsible for them.
Additional information about funding
This work was supported by the Academy of Finland, Projects No. 314764 (H. M.) and No. 321840 (T. L.). G. B., H. H. and T. L. are supported under the European Union’s Horizon 2020 research and innovation program by the European Research Council (ERC, Grant Agreement No. ERC-2015-CoG681707) and by the STRONG-2020 project (Grant Agreement No. 824093). The content of this article does not reflect the official opinion of the European Union and responsibility for the information and views expressed therein lies entirely with the authors. Computing resources from CSC–IT Center for Science in Espoo, Finland and from the Finnish Grid and Cloud Infrastructure (persistent identifier urn:nbn:fi:research-infras-2016072533) were used in this work.
Copyright© Authors, 2020