arXiv subjects

Junyi Han

Publications and source records attributed to Junyi Han.

7 recordsLinked to original sources

arXiv2026

Reconstructability and directed flow of short-lived resonances in Au+Au collisions at 19.6 and 200 GeV

We present a systematic study of the reconstructability and directed flow of hadronic resonances in Au+Au collisions within the UrQMD transport model. The main objective of this work is to investigate how the hadronic stage influences both resonance reconstructability and the final-state directed flow. A set of short-lived hadronic resonances, including $\rho^0$, $K^{*0}$, and $\Lambda(1520)$, is investigated to quantify their yields and reconstructable fractions as a function of charged-particle multiplicity, characterized by $(dN_{ch}/d\eta)^{1/3}$. We compare results at $\sqrt{s_{NN}} = 19.6$ and $200 ~\mathrm{GeV}$ to investigate possible energy-dependent differences in the reconstructability. Such differences reflect variations in the properties of the hadronic medium. The results are further examined as a function of resonance lifetime, revealing a clear ordering of reconstructability among different resonances. Overall, the reconstructability is found to be primarily governed by resonance lifetime. The directed-flow analysis reveals clear differences between resonances and their corresponding stable hadrons in mid-central collisions, while these differences become significantly weaker in peripheral collisions, highlighting the important role of hadronic evolution in shaping the final-state directed flow. These studies provide a unified picture of how the hadronic stage influences both resonance reconstructability and directed flow, offering new insights into resonance observables in relativistic heavy-ion collisions.

nucl-th↗

arXiv2026

Sequential Clusterization of Light Nuclei and Hypernuclei in Heavy-Ion Collisions within a Wigner Function Coalescence Framework

We investigate the formation of light nuclei and hypernuclei in Au+Au collisions at $\sqrt{s_{NN}}=3~\mathrm{GeV}$ within a coalescence framework embedded in the microscopic N-body Parton-Hadron-Quantum-Molecular Dynamics (PHQMD) transport model. The Wigner phase-space distributions employed in the coalescence calculation are constructed from realistic $N$-body wave functions obtained by solving the Schr\"odinger equation in the hyperspherical harmonics formalism, providing a solid and parameter-free description of nuclear clusters and hypernuclei. By comparing calculated rapidity distributions with STAR data, we extract species-dependent coalescence times, revealing a non-universal formation pattern among different clusters. The resulting yields and kinematic distributions of light nuclei and hypernuclei are systematically analyzed and shown to be sensitive to the underlying wave-function structure and formation time. In addition, we explore cluster-nucleon formation channels for $A=4$ systems. These additional channels improve the description of ${}^{4}\mathrm{He}$ and ${}^{4}_{\Lambda}\mathrm{H}$ yields and help address the underestimation of $A=4$ cluster production in theoretical approaches. Finally, we provide predictions for heavier hypernuclei, including ${}^{5}_{\Lambda}\mathrm{He}$ and ${}^{5}_{\Lambda\Lambda}\mathrm{He}$, which are of interest for future experimental measurements.

nucl-th↗

arXiv2026

DiagramRAG: A Lightweight Framework to Retrieve Scientific Diagram for Figure Generation

Scientific diagrams are essential for communicating complex methodologies in academic papers. A natural way for researchers to specify such diagrams is through rough sketches, where text labels, connectors, and spatial arrangements express early semantic and topological intentions. However, sketches are usually incomplete, making them insufficient for directly producing publication-quality diagrams. Existing sketch-based generation methods mainly reconstruct the sketch itself, while recent text-driven diagram generation frameworks rely on textual semantics and do not fully exploit the topological structure contained in sketches. In this paper, we introduce DiagramRAG, a lightweight retrieval-augmented framework for sketch-based scientific diagram completion. Given a user sketch, DiagramRAG retrieves reference diagrams that are both semantically relevant to the sketch content and topologically compatible with its structure, and uses them to guide downstream diagram generation. To enable efficient structure-aware retrieval, we represent diagrams as knowledge graphs, synthesize sketch variants at different simplification levels, and train an embedding model to align sketches with compatible diagrams in a shared space. The retrieved references further provide content, topology, and visual priors for completing and rendering the final diagram. Experiments show that DiagramRAG achieves F1-scores of 0.848 and 0.802 on DiagramBank and FigureBench, respectively, and improves generation quality with the best VLM-as-a-Judge score of 7.170, while reducing inference latency to 35.48 seconds per sample. Our code and data are available at https://anonymous.4open.science/r/DiagramRAG-A262 and https://huggingface.co/datasets/anonymous-review-a262/DiagramSketch.

cs.AI↗

arXiv2025

Investigation of the Spectator Effect on Light Nuclei Production in Nucleus-Nucleus Collisions at High Baryon Density Region

The light nuclei yields and their yield ratios, regarded as sensitive probes of the QCD phase structure, have been extensively measured at various collision energies. However, due to limited detector acceptance, the $p_{\rm T}$-integrated yield is often obtained by extrapolating from the measured $p_{\rm T}$ spectrum to the unmeasured low-$p_{\rm T}$ region using model-based fits. Simulations using AMPT-HC combined with an after-burner coalescence approach indicate a significant enhancement of light nuclei production at low $p_{\rm T}$, particularly in peripheral collisions and at forward rapidities, driven primarily by spectator nucleons. As a result, standard extrapolation procedures may systematically miss this additional low-$p_{\rm T}$ component, leading to an underestimate of the $p_{\rm T}$-integrated light-nucleus yields in such scenarios.

hep-ph↗

arXiv2025

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective

Out-Of-Distribution (OOD) generalization has gained increasing attentions for machine learning on graphs, as graph neural networks (GNNs) often exhibit performance degradation under distribution shifts. Existing graph OOD methods tend to follow the basic ideas of invariant risk minimization and structural causal models, interpreting the invariant knowledge across datasets under various distribution shifts as graph topology or graph spectrum. However, these interpretations may be inconsistent with real-world scenarios, as neither invariant topology nor spectrum is assured. In this paper, we advocate the learnable random walk (LRW) perspective as the instantiation of invariant knowledge, and propose LRW-OOD to realize graph OOD generalization learning. Instead of employing fixed probability transition matrix (i.e., degree-normalized adjacency matrix), we parameterize the transition matrix with an LRW-sampler and a path encoder. Furthermore, we propose the kernel density estimation (KDE)-based mutual information (MI) loss to generate random walk sequences that adhere to OOD principles. Extensive experiment demonstrates that our model can effectively enhance graph OOD generalization under various types of distribution shifts and yield a significant accuracy improvement of 3.87% over state-of-the-art graph OOD generalization baselines.

cs.LG↗

arXiv2024

Directed Flow of $\Lambda$, $^{3}_{\Lambda}{\rm H}$ and $^{4}_{\Lambda}{\rm H}$ in Au+Au collisions at $\sqrt{s_{\rm{NN}}}$ = 3.2, 3.5, 3.9 and 4.5 GeV at RHIC

Studying hyper-nuclei yields and their collectivity can shed light on their production mechanism as well as the hyperon-nucleon interactions. Heavy-ion collisions from the RHIC beam energy scan phase II (BES-II) provide an unique opportunity to understand these at high baryon densities. In these proceedings, we present a systematic study on energy dependence of the directed flow ($v_{1}$) for $\Lambda$ and hyper-nuclei ($^{3}_{\Lambda}{\rm H}$, $^{4}_{\Lambda}{\rm H}$) from mid-central Au+Au collisions at $\sqrt{s_{\mathrm{NN}}}$ = 3.2, 3.5, 3.9 and 4.5 GeV, collected by the STAR experiment with the fixed-target mode during BES-II. The rapidity (y) dependence of the hyper-nuclei $v_{1}$ is studied in mid-central collisions. The extracted $v_{1}$ slopes ($\mathrm{d}v_{1}/\mathrm{d}y|_{y=0}$) of the hyper-nuclei are positive and decrease gradually as the collision energy increases. These hyper-nuclei results are compared to that of light-nuclei including p, d, t/$\rm ^{3}He$ and $\rm ^{4}He$. Finally, these results are compared with a hadronic transport model including coalescence after-burner.

nucl-ex↗

arXiv2024

Towards Data-centric Machine Learning on Directed Graphs: a Survey

In recent years, Graph Neural Networks (GNNs) have made significant advances in processing structured data. However, most of them primarily adopted a model-centric approach, which simplifies graphs by converting them into undirected formats and emphasizes model designs. This approach is inherently limited in real-world applications due to the unavoidable information loss in simple undirected graphs and the model optimization challenges that arise when exceeding the upper bounds of this sub-optimal data representational capacity. As a result, there has been a shift toward data-centric methods that prioritize improving graph quality and representation. Specifically, various types of graphs can be derived from naturally structured data, including heterogeneous graphs, hypergraphs, and directed graphs. Among these, directed graphs offer distinct advantages in topological systems by modeling causal relationships, and directed GNNs have been extensively studied in recent years. However, a comprehensive survey of this emerging topic is still lacking. Therefore, we aim to provide a comprehensive review of directed graph learning, with a particular focus on a data-centric perspective. Specifically, we first introduce a novel taxonomy for existing studies. Subsequently, we re-examine these methods from the data-centric perspective, with an emphasis on understanding and improving data representation. It demonstrates that a deep understanding of directed graphs and their quality plays a crucial role in model performance. Additionally, we explore the diverse applications of directed GNNs across 10+ domains, highlighting their broad applicability. Finally, we identify key opportunities and challenges within the field, offering insights that can guide future research and development in directed graph learning.

cs.LG↗