M. A. Pérez de León, S. A. Ochoa-Oregon, D. F. Rentería-Estrada, R. J. Hernández-Pinto
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引用次数: 0
Abstract
The precise determination of fragmentation functions (FFs) of hadrons relies on the accurate description of the differential cross sections obtained from both experimental high-energy hadron colliders and theoretical predictions at higher orders in quantum chromodynamics. Various phenomenological strategies have been employed to extract FFs. In this work, we analyze the use of kinematical cuts for reactions including pions and kaons in proton-antiproton collisions to isolate individual FF contributions. This study examines the feasibility of using a similar approach as in proton-proton colliders to analyze FF flavour separation [1]. In particular, we study photon-hadron production at colliders, including NLO QCD and LO QED corrections to reconstruct the partonic momentum fractions.
期刊介绍:
The journal Few-Body Systems presents original research work – experimental, theoretical and computational – investigating the behavior of any classical or quantum system consisting of a small number of well-defined constituent structures. The focus is on the research methods, properties, and results characteristic of few-body systems. Examples of few-body systems range from few-quark states, light nuclear and hadronic systems; few-electron atomic systems and small molecules; and specific systems in condensed matter and surface physics (such as quantum dots and highly correlated trapped systems), up to and including large-scale celestial structures.
Systems for which an equivalent one-body description is available or can be designed, and large systems for which specific many-body methods are needed are outside the scope of the journal.
The journal is devoted to the publication of all aspects of few-body systems research and applications. While concentrating on few-body systems well-suited to rigorous solutions, the journal also encourages interdisciplinary contributions that foster common approaches and insights, introduce and benchmark the use of novel tools (e.g. machine learning) and develop relevant applications (e.g. few-body aspects in quantum technologies).