arXiv subjects

Xiaoyang Wei

Publications and source records attributed to Xiaoyang Wei.

7 recordsLinked to original sources

arXiv2026

AEON-z5: A Candidate AGN-driven Outflow Enriching the Circumgalactic Medium at $z\simeq5.23$

The dispersal of chemically enriched gas from galaxies into their surroundings is a key process in galaxy evolution, yet direct observational evidence at z>5 remains scarce. We present AEON-z5, a galaxy at z~5.23 in the COSMOS field comprising a compact continuum-emitting core surrounded by an extended, line-dominated ionized nebula. JWST/NIRCam imaging shows that H$α$+[N II] and H$β$+[O III] emission extends to projected radii of >4 kpc - 2-2.5 times the typical effective radius at the host stellar mass - reaching the outer ISM and the inner CGM. F444W grism data reveal a highly asymmetric H$α$+[N II] profile, which we interpret as a bipolar outflow and decompose into three kinematic components. The dominant component has a flux-weighted velocity offset of ~+489 km/s and FWHM~630 km/s, with a wing reaching $v_{84}$~792 km/s. A tentative detached feature at $Δv_{LOS}$~2800 km/s, detected at 1.5$σ$ in the 1D spectrum (2.4$σ$ in the 2D fit), may trace an outflow clump. These kinematics are most coherently explained by an AGN in the core. For the extended nebula, we derive a velocity curve shifting redward with radius, reaching 200-400 km/s at $r_p$~1.3-2.5 kpc - a trend that may reflect an accelerating outflow, corotating gas, or recycled inflow viewed in projection. Crucially, the measured [N II]/H$α$ ratios imply near-solar N2-based abundances (0.7-1.0 $Z_\odot$), remaining >0.37 $Z_\odot$ after allowing for AGN excitation and calibration systematics. The combination of large extent, enrichment, and extreme kinematics identifies AEON-z5 as a candidate snapshot of feedback-driven metal transport, offering a direct view of how early AGN activity may redistribute chemically processed gas into the CGM within the first ~1.1 billion years.

astro-ph.GA↗

arXiv2026

Early Emergence of Environmental Effects: Accelerated Galaxy Assembly in a z=2.96 Protocluster in the COSMOS Field

The redshift range z=2-4 marks a critical transition in large scale structure formation, where the dynamically unrelaxed progenitors of local massive clusters undergo rapid stellar mass assembly. We report the discovery and physical characterization of a highly significant protocluster, PC J1001+0214, at z=2.96 within the COSMOS field. Leveraging the multi-wavelength COSMOS2025 catalog with exceptional photometric precision in conjunction with JWST/NIRCam wide-field slitless spectroscopy (WFSS) from the COSMOS-3D program, we robustly identify a cosmic overdensity with $δ=2.66$. The structure comprises 131 member galaxies, including 21 spectroscopically confirmed members (identified primarily via He I $\lambda10830$ emission) and 110 high-fidelity photometric members. A comparative analysis against a mass-complete coeval field sample reveals a statistically significant +0.2 dex shift in the stellar mass distribution of protocluster members, signaling accelerated mass assembly in the dense environment by $z\sim3$. While the protocluster population broadly follows the star-forming main sequence, low-to-intermediate mass galaxies ($\log_{10}(M_{*}/M_{\odot})\le9.7$) exhibit a measurable star formation rate (SFR) enhancement of +0.11 to +0.15 dex. Crucially, the quiescent fraction remains extremely low and indistinguishable from the field sample, implying that environmental quenching mechanisms have not yet become dominant. Furthermore, a preliminary size-mass analysis hints at elevated morphological compactness among protocluster members at fixed stellar mass. These results suggest that PC J1001+0214 represents a growth-dominated protocluster phase in which environmental effects are already detectable, primarily through accelerated stellar mass assembly and ongoing growth rather than through strong quenching.

astro-ph.GA↗

arXiv2026

Pre-Virialized Assembly at Cosmic Dawn: The Dynamics and Extreme Ionization of Compact Group CGG-z7 at $z\sim7.04$

We report the discovery of CGG-z7, the most compact galaxy group at $z\gtrsim7$ identified to the north of the GOODS-North field, observed by the JWST POPPIES program. The system consists of at least six members within a projected size of $7.8\times5.7$ kpc$^2$, four of which are spectroscopically confirmed via [O III] and H$β$ emission. The group exhibits a low line-of-sight velocity dispersion ($\approx93.7$ km s$^{-1}$) relative to its substantial stellar mass ($M_* \approx 10^{9.8} M_{\odot}$), yielding a stellar-to-dynamical mass ratio of $M_*/M_{\mathrm{vir}} \approx 0.15$. This ratio, exceeding typical values for virialized halos by a factor of $3$, indicates that the system is highly likely not in dynamical equilibrium. Instead, we interpret CGG-z7 as a pre-virialized structure, likely a major merger caught near apocenter -- capturing the rapid, chaotic formation of a massive "Red Nugget". Spectroscopic analysis reveals extreme ionization conditions and low metallicity across the group. In particular, the central galaxy reaches an extraordinarily high [O III]/H$β$ ratio of $\sim18$, which is likely indicative of an obscured AGN. CGG-z7 thus serves as a unique laboratory for the physics of pre-virialized galaxy assembly, bridging the gap between turbulent high-$z$ assembly and the quiescent galaxies seen at cosmic noon.

astro-ph.GA↗

arXiv2025

QwenCLIP: Boosting Medical Vision-Language Pretraining via LLM Embeddings and Prompt tuning

Contrastive Language-Image Pretraining (CLIP) has demonstrated strong generalization for vision-language tasks in computer vision and medical domains, yet its text encoder accepts only up to 77 tokens, which limits its ability to represent long and information-rich radiology reports. Recent adaptations using domain-specific encoders, such as PubMedBERT or ClinicalBERT, mitigate this issue by leveraging medical corpora, but remain constrained by their limited input length (typically 512 tokens) and relatively shallow semantic understanding. To address these limitations, we propose QwenCLIP, a vision-language framework that replaces CLIP's text encoder with a large language model (LLM)-based embedding module (e.g., Qwen3-Embedding) and introduces learnable prompts to enhance cross-modal alignment. By leveraging the extended context window and richer representations of LLMs, QwenCLIP captures comprehensive medical semantics from long-form clinical text, substantially improving medical image-text alignment and downstream performance on radiology benchmarks. Our code is publicly available at https://github.com/Wxy-24/QwenCLIP.

cs.CV↗

arXiv2025

Factor Augmented Quantile Regression Model

Along with the widespread adoption of high-dimensional data, traditional statistical methods face significant challenges in handling problems with high correlation of variables, heavy-tailed distribution, and coexistence of sparse and dense effects. In this paper, we propose a factor-augmented quantile regression (FAQR) framework to address these challenges simultaneously within a unified framework. The proposed FAQR combines the robustness of quantile regression and the ability of factor analysis to effectively capture dependencies among high-dimensional covariates, and also provides a framework to capture dense effects (through common factors) and sparse effects (through idiosyncratic components) of the covariates. To overcome the lack of smoothness of the quantile loss function, convolution smoothing is introduced, which not only improves computational efficiency but also eases theoretical derivation. Theoretical analysis establishes the accuracy of factor selection and consistency in parameter estimation under mild regularity conditions. Furthermore, we develop a Bootstrap-based diagnostic procedure to assess the adequacy of the factor model. Simulation experiments verify the rationality of FAQR in different noise scenarios such as normal and $t_2$ distributions.

stat.ME↗

arXiv2025

A Semantically-Aware Relevance Measure for Content-Based Medical Image Retrieval Evaluation

Performance evaluation for Content-Based Image Retrieval (CBIR) remains a crucial but unsolved problem today especially in the medical domain. Various evaluation metrics have been discussed in the literature to solve this problem. Most of the existing metrics (e.g., precision, recall) are adapted from classification tasks which require manual labels as ground truth. However, such labels are often expensive and unavailable in specific thematic domains. Furthermore, medical images are usually associated with (radiological) case reports or annotated with descriptive captions in literature figures, such text contains information that can help to assess CBIR.Several researchers have argued that the medical concepts hidden in the text can serve as the basis for CBIR evaluation purpose. However, these works often consider these medical concepts as independent and isolated labels while in fact the subtle relationships between various concepts are neglected. In this work, we introduce the use of knowledge graphs to measure the distance between various medical concepts and propose a novel relevance measure for the evaluation of CBIR by defining an approximate matching-based relevance score between two sets of medical concepts which allows us to indirectly measure the similarity between medical images.We quantitatively demonstrate the effectiveness and feasibility of our relevance measure using a public dataset.

cs.CV↗

arXiv2024

Prediction of Vessel Arrival Time to Pilotage Area Using Multi-Data Fusion and Deep Learning

This paper investigates the prediction of vessels' arrival time to the pilotage area using multi-data fusion and deep learning approaches. Firstly, the vessel arrival contour is extracted based on Multivariate Kernel Density Estimation (MKDE) and clustering. Secondly, multiple data sources, including Automatic Identification System (AIS), pilotage booking information, and meteorological data, are fused before latent feature extraction. Thirdly, a Temporal Convolutional Network (TCN) framework that incorporates a residual mechanism is constructed to learn the hidden arrival patterns of the vessels. Extensive tests on two real-world data sets from Singapore have been conducted and the following promising results have been obtained: 1) fusion of pilotage booking information and meteorological data improves the prediction accuracy, with pilotage booking information having a more significant impact; 2) using discrete embedding for the meteorological data performs better than using continuous embedding; 3) the TCN outperforms the state-of-the-art baseline methods in regression tasks, exhibiting Mean Absolute Error (MAE) ranging from 4.58 min to 4.86 min; and 4) approximately 89.41% to 90.61% of the absolute prediction residuals fall within a time frame of 10 min.

cs.LG↗