arXiv subjects

Zhixiang Yang

Publications and source records attributed to Zhixiang Yang.

4 recordsLinked to original sources

arXiv2026

Description of Charged\text{-}Particle Multiplicity Distributions in High\text{-}Energy Proton\text{-}Proton Collisions Based on a Two-Component Model and Examination of Parton Distribution Functions

High-energy proton-proton collisions at the LHC offer a stringent test of Quantum Chromodynamics (QCD) in the small-$x$, gluon-dominated regime. This study focus on a minimal, gluon-driven framework to describe the charged-particle multiplicities and their pseudorapidity densities in high energy collisions. The two-component model taken here includes the hard gluon-gluon fusion process and the soft quark recombination process, which directly relates to both integrated and unintegrated parton distributions. We begin by evolving Parton Distribution Functions (PDFs) using the Modified Dokshitzer-Gribov-Lipatov-Altarelli-Parisi (MD-DGLAP) equations. These PDFs are then converted into unintegrated PDFs (UPDFs) via the Kimber-Martin-Ryskin (KMR) scheme. The resulting PDFs and UPDFs are incorporated into the two-component model to predict the charged-particle pseudorapidity density $\left(1 / N_{\mathrm{ev}}\right) d N_{\mathrm{ch}} / d \eta$ in $pp$ collisions at LHC energies. Our predictions are compared to the data from the ATLAS experiment, revealing that the model effectively captures the features of the observed pseudorapidity distributions, despite its simplicity. Within this framework, the gluon-gluon fusion processes are found to dominate particle production for $\sqrt { s } \ge 9 0 0 \ \mathrm { GeV }$.These findings provide phenomenological support for MD-DGLAP-based PDFs and the associated small-$x$ gluon dynamics. Furthermore,a comparative analysis of results from alternative PDF sets--including CTEQ, MSHT, NNPDF, HERAPDF, and GRV--is performed, with particular focus on examining their consistency with the relative shapes of experiment data in the small-$x$ region.

hep-ph↗

arXiv2024

Generative AI Enabled Matching for 6G Multiple Access

In wireless networks, applying deep learning models to solve matching problems between different entities has become a mainstream and effective approach. However, the complex network topology in 6G multiple access presents significant challenges for the real-time performance and stability of matching generation. Generative artificial intelligence (GenAI) has demonstrated strong capabilities in graph feature extraction, exploration, and generation, offering potential for graph-structured matching generation. In this paper, we propose a GenAI-enabled matching generation framework to support 6G multiple access. Specifically, we first summarize the classical matching theory, discuss common GenAI models and applications from the perspective of matching generation. Then, we propose a framework based on generative diffusion models (GDMs) that iteratively denoises toward reward maximization to generate a matching strategy that meets specific requirements. Experimental results show that, compared to decision-based AI approaches, our framework can generate more effective matching strategies based on given conditions and predefined rewards, helping to solve complex problems in 6G multiple access, such as task allocation.

cs.NI↗

arXiv2024

Explaining muon excess in cosmic rays using the gluon condensation model

Ultrahigh-energy cosmic rays are often characterized indirectly by analyzing the properties of secondary cosmic ray particles produced in the collisions with air nuclei. The particle number $N_μ$ of muon and the depth of shower maximum $X_\mathrm{max}$ after air shower cascade are mostly studied to infer the energy and mass of the incident cosmic rays. Research have shown that there is a significant excess in the observed number of muons arriving at the ground from extensive air showers (EAS) compared to the simulations using the existing cosmic ray hadronic interaction model. To explain this muon excess phenomenon, a new theoretical model, the gluon condensation model (GC model), is introduced in this paper and simulated by using the AIRES engine. We assume that the GC effect appears mainly in the first collision of the cascade leading to a significant increase in the strangeness production, consequently, the production rate of kaons is increased and $n_K/n_π$ is greater than the value of the usual hadronic interaction process. In the calculation, the model assumes that only pions and kaons are produced in GC state. The increase of strange particle yield would mean that the energy transferred from the hadronic cascade to electromagnetic cascade through $π^{0} \rightarrow 2γ$ decay is reduced. This would in turn increase the number of muons at the ground level due to meson decays.Our model provides a new theoretical possibility to explain the muon excess puzzle.

astro-ph.HE↗

arXiv2024

Revolutionizing Wireless Networks with Self-Supervised Learning: A Pathway to Intelligent Communications

With the rapid proliferation of mobile devices and data, next-generation wireless communication systems face stringent requirements for ultra-low latency, ultra-high reliability, and massive connectivity. Traditional AI-driven wireless network designs, while promising, often suffer from limitations such as dependency on labeled data and poor generalization. To address these challenges, we present an integration of self-supervised learning (SSL) into wireless networks. SSL leverages large volumes of unlabeled data to train models, enhancing scalability, adaptability, and generalization. This paper offers a comprehensive overview of SSL, categorizing its application scenarios in wireless network optimization and presenting a case study on its impact on semantic communication. Our findings highlight the potentials of SSL to significantly improve wireless network performance without extensive labeled data, paving the way for more intelligent and efficient communication systems.

eess.SP↗