arXiv subjects

Zhanpeng Zhu

Publications and source records attributed to Zhanpeng Zhu.

4 recordsLinked to original sources

arXiv2026

Discovery of an Unbound Flyby Companion of UBC 63: In the Immediate Aftermath of a Close Encounter

We re-investigate the open cluster UBC 63 using the Gaia DR3 data and show that, rather than being a single cluster as previously classified, it is a compelling candidate for a double cluster undergoing an unbound flyby interaction. A GMM decomposition performed in the 5D astrometric space reveals the two statistically distinct components, namely UBC 63A (98 members, Age = 21 $\pm$ 4 Myr) and UBC 63B (148 members, Age = 562 $\pm$ 43 Myr). A significant age difference of $Δ\mathrm{Age} = 541 \pm 43$ Myr between the clusters, rules out coeval formation. Their 3D separation of $60 \pm 29$ pc at the birth-epoch of the younger cluster, indicates that the clusters might have originated from the same molecular cloud complex. At present, the system exhibits a 3D separation of $26 \pm 8$ pc, with a relative velocity of $3.60 \pm 1.80$ km s$^{-1}$. Orbital integrations and \textit{N}-body simulations of the pair suggest that the systems had a close encounter, reaching a separation of $7 \pm 2$ pc only $\sim$~6 Myr ago and predict a rapid divergence to a separation of $491 \pm 213$ pc within the next $\sim$100 Myr. The low escape velocity ($V_{\rm esc} = 0.51 \pm 0.12$ km s$^{-1}$) of the system compared to the relative 3D velocity indicates that they are gravitationally unbound. Their low tidal factors, elongated structures and populations extending beyond the Jacobi radii may reflect a strong transient tidal interaction between the clusters.

astro-ph.GA↗

arXiv2026

KidMesh: Computational Mesh Reconstruction for Pediatric Congenital Hydronephrosis Using Deep Neural Networks

Pediatric congenital hydronephrosis (CH) is a common urinary tract disorder, primarily caused by obstruction at the renal pelvis-ureter junction. Magnetic resonance urography (MRU) can visualize hydronephrosis, including renal pelvis and calyces, by utilizing the natural contrast provided by water. Existing voxel-based segmentation approaches can extract CH regions from MRU, facilitating disease diagnosis and prognosis. However, these segmentation methods predominantly focus on morphological features, such as size, shape, and structure. To enable functional assessments, such as urodynamic simulations, external complex post-processing steps are required to convert these results into mesh-level representations. To address this limitation, we propose an end-to-end method based on deep neural networks, namely KidMesh, which could automatically reconstruct CH meshes directly from MRU. Generally, KidMesh extracts feature maps from MRU images and converts them into feature vertices through grid sampling. It then deforms a template mesh according to these feature vertices to generate the specific CH meshes of MRU images. Meanwhile, we develop a novel schema to train KidMesh without relying on accurate mesh-level annotations, which are difficult to obtain due to the sparsely sampled MRU slices. Experimental results show that KidMesh could reconstruct CH meshes in an average of 0.4 seconds, and achieve comparable performance to conventional methods without requiring post-processing. The reconstructed meshes exhibited no self-intersections, with only 3.7% and 0.2% of the vertices having error distances exceeding 3.2mm and 6.4mm, respectively. After rasterization, these meshes achieved a Dice score of 0.86 against manually delineated CH masks. Furthermore, these meshes could be used in renal urine flow simulations, providing valuable urodynamic information for clinical practice.

cs.CV↗

arXiv2025

A study of newly discovered close binary open clusters in the Milky Way

With the release of Gaia data, the number of known Galactic open clusters (OCs) has increased rapidly, providing an excellent opportunity to confirm more binary open clusters in the Milky Way. Using a recently released OC catalogue, we employed the photometric and astrometric data of OCs and their member stars to find close binary open clusters (CBOCs). The three dimensional spatial coordinates, proper motions, and colour-magnitude diagrams (CMDs) are used for identifying candidate CBOCs. The fundamental parameters of 26 star clusters are determined by fitting CMDs to stellar population isochrones, to check the similarity of reddenings, ages and metallicities of the sub-clusters of candidate CBOCs. The virial equilibrium is then used to exclude fake CBOCs including unbound moving groups. To further confirm the binary nature of the CBOC candidates, we calculated their Roche radii and orbital parameters. The tidal radius and radial velocity difference are then compared to the Roche radius and orbital velocity respectively, to find out gravitationally bound pairs. We identified nine new CBOC candidates from bound candidate open clusters, seven of which are shown to be candidates for primordial binary open clusters (PBOCs). However, only the pair CWNU 1024 and OCSN 82 is identified as a gravitationally bound CBOC, when considering the uncertainties. The other eight CBOC candidates appear to be gravitationally unbound pairs, but the results depend on the methods of tidal radius determination and gravitational binding examination.

astro-ph.GA↗

arXiv2025

Resource Allocation and Pricing for Blockchain-enabled Metaverse: A Stackelberg Game Approach

As the next-generation Internet paradigm, the metaverse can provide users with immersive physical-virtual experiences without spatial limitations. However, there are various concerns to be overcome, such as resource allocation, resource pricing, and transaction security issues. To address the above challenges, we integrate blockchain technology into the metaverse to manage and automate complex interactions effectively and securely utilizing the advantages of blockchain. With the objective of promoting the Quality of Experience (QoE), Metaverse Service Users (MSUs) purchase rendering and bandwidth resources from the Metaverse Service Provider (MSP) to access low-latency and high-quality immersive services. The MSP maximizes the profit by controlling the unit prices of resources. In this paper, we model the interaction between the MSP and MSUs as a Stackelberg game, in which the MSP acts as the leader and MSUs are followers. The existence of Stackelberg equilibrium is analyzed and proved mathematically. Besides, we propose an efficient greedy-and-search-based resource allocation and pricing algorithm (GSRAP) to solve the Stackelberg equilibrium (SE) point. Finally, we conduct extensive simulations to verify the effectiveness and efficiency of our designs. The experiment results show that our algorithm outperforms the baseline scheme in terms of improving the MSP's profit and convergence speed.

cs.GT↗