Papers
Topics
Authors
Recent
AI Research Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 67 tok/s
Gemini 2.5 Pro 36 tok/s Pro
GPT-5 Medium 16 tok/s Pro
GPT-5 High 18 tok/s Pro
GPT-4o 66 tok/s Pro
Kimi K2 170 tok/s Pro
GPT OSS 120B 440 tok/s Pro
Claude Sonnet 4 36 tok/s Pro
2000 character limit reached

Employing RHIC and LHC data to determine TMD gluon density in a proton (1806.06739v1)

Published 18 Jun 2018 in hep-ph

Abstract: Transverse momentum dependent (TMD) parton distributions in a proton are important in high energy physics from both theoretical and phenomenological points of view. Using the latest RHIC and LHC data on the inclusive soft hadron production in $pp$ and $AA$ collisions at small transverse momenta, we determine the parameters of the initial TMD gluon density, derived in the framework of quark-gluon string model at the low scale $\mu_0 \sim 1 - 2$ GeV and refine its large-$x$ behaviour using the LHC data on the $t \bar t$ production at $\sqrt s = 13$ TeV. Then, we apply the Catani-Ciafaloni-Fiorani-Marchesini (CCFM) evolution equation to extend the obtained TMD gluon density to the whole kinematical region. In addition, the complementary TMD valence and sea quark distributions are generated. The latter are evaluated in the approximation where the gluon-to-quark splitting occurs at the last evolution step using the TMD gluon-to-quark splitting function. Several phenomenological applications of the proposed TMD quark and gluon densities to the LHC processes are discussed.

Summary

We haven't generated a summary for this paper yet.

Lightbulb On Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube