arXiv subjects

Conghao Zhou

Publications and source records attributed to Conghao Zhou.

At least 19 recordsLinked to original sources

arXiv2026

Probing Projection Effects in Optically-Selected Clusters with Velocity Dispersions Outliers

The evolution of galaxy cluster abundance is a powerful probe of cosmology, but its efficacy depends on our ability to understand and control for systematics in the selection and mass estimation of clusters. We study redMaPPer-selected clusters in the Cardinal simulation, which simulates the DES Y3 observational conditions. Specifically, we investigate the line-of-sight velocity distributions of cluster member galaxies in 12,272 simulated clusters. We find that a significant fraction of clusters, 35%, have velocity dispersions that are high compared to their richness with an offset of $\sim 400$ km s$^{-1}$ between the outliers and the main population. A similar population of velocity dispersion outliers was found in the DES Y3 data. Nearly all of the outliers (98%) in a low-richness subsample have non-Gaussian velocity distributions, and they are more likely than the main population to have significant velocity substructure. These clusters also tend to have higher redshifts than clusters with typical velocity dispersions, and they show a larger scatter between halo mass and richness. Overall, our results point to a significant contamination of redMaPPer-selected samples from line of sight structure, particularly at high redshifts and low richness. Comparison to the Buzzard simulations shows similar results.

astro-ph.CO↗

arXiv2026

Cluster finding with outskirt stellar masses and percolation

The abundance of galaxy clusters is a powerful cosmological probe, but optical cluster cosmology is limited by selection systematics, in particular the projection effects that affect cluster finders based on galaxy populations such as the red sequence. The outer stellar mass ($M_\mathrm{out}$) of cluster central galaxies -- e.g., the stellar mass in a 50-100 kpc annulus -- offers an alternative selection that relies only on the central galaxy and is therefore largely free from projection effects. Its primary systematic is instead satellite contamination, since massive clusters can host more than one galaxy with high outer stellar mass. Using the IllustrisTNG300 simulation at $z=0.4$, we quantify this contamination and investigate a simple, proximity-based percolation method to mitigate it, in which galaxies with lower outer stellar mass lying within a given radius of a more massive galaxy are removed from the sample. We find that the satellite fraction defined by the friends-of-friends (FoF) algorithm is modest even without percolation ($\leq 15\%$ for $M_\mathrm{out} > 10^{10}\,\mathrm{M}_\odot$ and $<10\%$ for $M_\mathrm{out} > 10^{11}\,\mathrm{M}_\odot$), and that percolation reduces it further, with the improvement increasing for percolation radii up to $3.0\,R_{200c}$. For a moderately high outer stellar mass cut ($\sim 4\times10^{10}\,\mathrm{M}_\odot$) and percolation radius ($\sim 2.0\,R_{200c}$), we recover a cluster sample that is both highly complete and pure for halo masses $\gtrsim 10^{14}\,\mathrm{M}_\odot$. These results indicate that outer stellar mass, combined with simple percolation, has the potential to provide a clean and readily calibratable selection of massive galaxy clusters.

astro-ph.CO↗

arXiv2026

RadioVIL: Anomaly-Aware Diffusion Models for Radio Map Inpainting and Zero-Shot Vehicle Localization

High-precision radio map construction is essential for emerging 6G Integrated Sensing and Communication (ISAC) applications, including digital twins and intelligent transportation. However, existing deep learning methods predominantly treat this as a pure image completion task, resulting in over-smoothed reconstructions that fundamentally erase high-frequency scattering signatures of dynamic physical entities such as hidden vehicles. To overcome this, we propose RadioVIL, an efficient two-stage framework that reformulates joint radio map inpainting and zero-shot vehicle localization as a prior-guided physical inverse problem. Specifically, we first train a Denoising Diffusion Probabilistic Model (DDPM) to capture the structural generative prior of the environment. During inference from highly sparse measurements, we employ a Diffusion-based Mediating Intermediate Layer Optimization (DMILO) algorithm. By optimizing an L1-regularized sparse deviation term, DMILO mathematically isolates vehicle scattering anomalies layer-by-layer without unfolding the entire denoising chain. Extensive experiments demonstrate that while conventional reconstruction baselines fail to detect hidden vehicles, and the zero-shot diffusion baseline achieves only limited detection ability due to forced semantic harmonization, RadioVIL preserves authentic physical textures, yielding the best LPIPS of 0.0587 in our evaluation. Uniquely, it unlocks accurate zero-shot vehicle localization directly from sparse radio maps, securing a 75.20% Recall and a 3.31-meter average error, paving a robust way for ISAC at the 6G edge.

eess.SP↗

arXiv2026

OASIS: Observation-Aware Simulation-Based Inference via Distributional Matching

We introduce OASIS, a simulation-based inference framework for scientific settings where observations are distorted by measurement error, selection effects, and other survey-specific transformations. In many real applications, simulators generate latent, noiseless quantities, while the data are observed only after passing through a complex observational pipeline. Standard simulation-based inference methods often ignore this distinction, comparing observations to idealized simulator outputs or relying on low-dimensional summaries that can miss important structure. OASIS addresses this mismatch by explicitly embedding the observation model into the simulator and performing inference directly at the level of observed-data distributions. The method constructs a pseudo-posterior by reweighting prior samples according to a maximum mean discrepancy (MMD) loss between the empirical distributions of the observed data and forward-simulated observations, thereby avoiding both handcrafted summaries and learned neural surrogates. We provide theoretical guarantees for Monte Carlo consistency, convergence of the empirical pseudo-posterior to its population counterpart, and posterior concentration on the MMD-identified parameter set, with consistency for the true parameter under correct specification and identifiability. In controlled errors-in-variables regression experiments, OASIS delivers robust parameter recovery and well-calibrated uncertainty under heterogeneous and non-Gaussian measurement noise. We then demonstrate the method on a realistic cosmological application involving galaxy cluster observations across multiple wavelengths, in which latent physical properties are linked to observables through nonlinear scaling relations, heteroscedastic errors, selection functions, and incomplete coverage.

stat.ME↗

arXiv2026

Accurate Galaxy Cluster Shear and Mass Calibration for LSST with AnaCal

The observed abundance of galaxy clusters as a function of mass and redshift provides a powerful route to precision cosmology; a key challenge for cluster cosmology is to establish the relation between cluster observables and cluster masses, for which cluster weak gravitational lensing has become the standard tool. A key challenge for cluster lensing is that the shear signal near cluster centers can reach the non-linear regime, where many shear estimators rely on perturbative assumptions that must be explicitly validated. In this work, we use image simulations to test the performance of the shear estimator AnaCal for cluster weak lensing under conditions representative of the 10-year LSST data. We find that AnaCal recovers the input shear with minimal bias even at mildly high shear, $|g|\sim 0.15$. We discover a radially decreasing mean shear response as seen previously in data, driven by the radial dependence of the convergence field; if unmodeled, this effect can bias shear inference. We also find a positive shear-estimation bias at third order in the reduced shear near the cluster center. However, because only a small fraction of galaxies lie in the high-shear regime and those measurements are further downweighted by the covariance matrix, the resulting mean cluster-mass bias for cluster lens masses in $[10^{14} M_\odot, 10^{15} M_\odot]$ -- adopting a scale cut of $\sim 0.2$ Mpc at $z=0.25$ -- is $0.24 \pm 0.26\%$ under ideal settings. These results demonstrate that AnaCal is a robust tool for accurate cluster mass calibration in the LSST era.

astro-ph.CO↗

arXiv2026

Beam-Aware Radio Map Estimation With Physics-Consistent Parametric Modeling for Unknown Multiple Satellites

Satellite networks with dense low Earth orbit (LEO) constellations rely on aggressive spectrum reuse, making co-channel interference a dominant and rapidly varying factor that limits link availability and complicates spectrum sharing and compliance. Satellite radio map (RM) construction is therefore essential for interference cognition, yet it is challenging because the active satellite set is unknown, beam footprints and pointing are not directly observable, and received signal strength (RSS) measurements are difficult to calibrate under coupled link budget variations and noise. These latent uncertainties yield a severely underdetermined inverse problem with strong signature coherence, where existing methods often trade detection recall for precision and still fail to recover a faithful continuous RSS field. This paper proposes a beam-aware RM estimation framework that unifies active satellite identification and RSS field reconstruction through physics-consistent parametric modeling. An interpretable structural prior links geometry and beam shaping to spatial RSS formation, and an adaptive model order selection strategy infers the number of active satellites from measurements by balancing fit and complexity. Extensive experiments across varying signal to noise ratio (SNR), total satellite count, and active satellite count demonstrate consistently higher RSS spatial correlation, lower root mean squared error (RMSE), and improved F1 score, validating the proposed approach for interference-aware satellite RM construction in satellite networks.

cs.IT↗

arXiv2026

Physics-informed line-of-sight learning for scalable deterministic channel modeling

Deterministic channel modeling maps a physical environment to its site-specific electromagnetic response. Ray tracing produces complete multi-dimensional channel information but remains prohibitively expensive for area-wide deployment. We identify line-of-sight (LoS) region determination as the dominant bottleneck. To address this, we propose D$^2$LoS, a physics-informed neural network that reformulates dense pixel-level LoS prediction into sparse vertex-level visibility classification and projection point regression, avoiding the spectral bias at sharp boundaries. A geometric post-processing step enforces hard physical constraints, yielding exact piecewise-linear boundaries. Because LoS computation depends only on building geometry, cross-band channel information is obtained by updating material parameters without retraining. We also construct RayVerse-100, a ray-level dataset spanning 100 urban scenarios with per-ray complex gain, angle, delay, and geometric trajectory. Evaluated against rigorous ray tracing ground truth, D$^2$LoS achieves 3.28~dB mean absolute error in received power, 4.65$^\circ$ angular spread error, and 20.64~ns delay spread error, while accelerating visibility computation by over 25$\times$.

cs.IT↗

arXiv2026

A Tutorial on Learning-Based Radio Map Construction: Data, Paradigms, and Physics-Awareness

Radio maps (RMs) provide the digital representation of the wireless propagation environment, mapping complex geographical and topological boundary conditions to critical spatial-spectral metrics that range from received signal strength to full channel state information matrices. The integration of artificial intelligence into next generation wireless networks further necessitates the accurate construction of RMs as a foundational prerequisite for electromagnetic digital twins. This paper presents a comprehensive survey of learning-based RM construction, systematically addressing three intertwined dimensions: data, paradigms, and physics-awareness. From the data perspective, we review physical measurement campaigns, ray tracing simulation engines, and publicly available benchmark datasets, identifying their respective strengths and fundamental limitations. From the paradigm perspective, we establish a core taxonomy that categorizes RM construction into source-aware forward prediction and source agnostic inverse reconstruction, and examine five principal neural architecture families spanning convolutional neural networks, vision transformers, graph neural networks, generative adversarial networks, and diffusion models. We further survey optics-inspired methods adapted from neural radiance fields and 3D Gaussian splatting for continuous wireless radiation field modeling. From the physics-awareness perspective, we introduce a three-level integration framework encompassing data-level feature engineering, loss-level partial differential equation regularization, and architecture level structural isomorphism. Open challenges including foundation model development, physical hallucination detection, and mortized inference for real-time deployment are discussed to outline future research directions. The project page is at https://github.com/UNIC-Lab/Awesome-Radio-Map-Categorized.

eess.SY↗

arXiv2026

Resource-Efficient Reinforcement for Reasoning Large Language Models via Dynamic One-Shot Policy Refinement

Large language models (LLMs) have exhibited remarkable performance on complex reasoning tasks, with reinforcement learning under verifiable rewards (RLVR) emerging as a principled framework for aligning model behavior with reasoning chains. Despite its promise, RLVR remains prohibitively resource-intensive, requiring extensive reward signals and incurring substantial rollout costs during training. In this work, we revisit the fundamental question of data and compute efficiency in RLVR. We first establish a theoretical lower bound on the sample complexity required to unlock reasoning capabilities, and empirically validate that strong performance can be achieved with a surprisingly small number of training instances. To tackle the computational burden, we propose Dynamic One-Shot Policy Refinement (DoPR), an uncertainty-aware RL strategy that dynamically selects a single informative training sample per batch for policy updates, guided by reward volatility and exploration-driven acquisition. DoPR reduces rollout overhead by nearly an order of magnitude while preserving competitive reasoning accuracy, offering a scalable and resource-efficient solution for LLM post-training. This approach offers a practical path toward more efficient and accessible RL-based training for reasoning-intensive LLM applications.

cs.AI↗

arXiv2026

RadioDiff-Flux: Efficient Radio Map Construction via Generative Denoise Diffusion Model Trajectory Midpoint Reuse

Accurate radio map (RM) construction is essential to enabling environment-aware and adaptive wireless communication. However, in future 6G scenarios characterized by high-speed network entities and fast-changing environments, it is very challenging to meet real-time requirements. Although generative diffusion models (DMs) can achieve state-of-the-art accuracy with second-level delay, their iterative nature leads to prohibitive inference latency in delay-sensitive scenarios. In this paper, by uncovering a key structural property of diffusion processes: the latent midpoints remain highly consistent across semantically similar scenes, we propose RadioDiff-Flux, a novel two-stage latent diffusion framework that decouples static environmental modeling from dynamic refinement, enabling the reuse of precomputed midpoints to bypass redundant denoising. In particular, the first stage generates a coarse latent representation using only static scene features, which can be cached and shared across similar scenarios. The second stage adapts this representation to dynamic conditions and transmitter locations using a pre-trained model, thereby avoiding repeated early-stage computation. The proposed RadioDiff-Flux significantly reduces inference time while preserving fidelity. Experiment results show that RadioDiff-Flux can achieve up to 50 acceleration with less than 0.15% accuracy loss, demonstrating its practical utility for fast, scalable RM generation in future 6G networks.

cs.LG↗

arXiv2025

BEVCooper: Accurate and Communication-Efficient Bird's-Eye-View Perception in Vehicular Networks

Bird's-Eye-View (BEV) is critical to connected and automated vehicles (CAVs) as it can provide unified and precise representation of vehicular surroundings. However, quality of the raw sensing data may degrade in occluded or distant regions, undermining the fidelity of constructed BEV map. In this paper, we propose BEVCooper, a novel collaborative perception framework that can guarantee accurate and low-latency BEV map construction. We first define an effective metric to evaluate the utility of BEV features from neighboring CAVs. Then, based on this, we develop an online learning-based collaborative CAV selection strategy that captures the ever-changing BEV feature utility of neighboring vehicles, enabling the ego CAV to prioritize the most valuable sources under bandwidth-constrained vehicle-to-vehicle (V2V) links. Furthermore, we design an adaptive fusion mechanism that optimizes BEV feature compression based on the environment dynamics and real-time V2V channel quality, effectively balancing feature transmission latency and accuracy of the constructed BEV map. Theoretical analysis demonstrates that, BEVCooper achieves asymptotically optimal CAV selection and adaptive feature fusion under dynamic vehicular topology and V2V channel conditions. Extensive experiments on real-world testbed show that, compared with state-of-the-art benchmarks, the proposed BEVCooper enhances BEV perception accuracy by up to $63.18\%$ and reduces end-to-end latency by $67.9\%$, with only $1.8\%$ additional computational overhead.

cs.NI↗

arXiv2025

Reaching for the Edge II: Stellar Halos out to Large Radii as a Tracer of Dark Matter Halo Mass

The diffuse outskirts of brightest cluster galaxies (BCGs) encode valuable information about the assembly history and mass of their host dark matter halos. However, the low surface brightness of these stellar halos has historically made them difficult to observe. Recent deep imaging, particularly with Hyper Suprime-Cam (HSC), has shown that the stellar mass within relatively large projected annuli, such as within $50$ and $100$ kpc, is a promising proxy for halo mass. However, the optimal radial definition of this "outskirt mass" remains uncertain. We construct an HSC-like mock observing pipeline to measure the stellar mass density profiles of BCGs in the IllustrisTNG simulations. Our mock observations closely reproduce HSC profiles across six orders of magnitude in surface density. We then systematically measure stellar masses within different annuli and how tightly they are connected to halo mass. We find that stellar masses measured within simple apertures exhibit considerably more scatter in the stellar mass-halo mass relation than those measured within projected ellipsoidal annuli. We identify an optimal range of definitions, with inner radii between $\sim 70$-$200$ kpc and outer radii between $\sim 125$-$500$ kpc. We also introduce two halo-mass-dependent S\'ersic models for the average stellar halo profiles. We present a S\'ersic-based fitting function that describes the profiles as a function of the halo mass, $M_{\rm vir}$, with a median error of $54\%$. Adding the central stellar mass of the BCG as a second parameter slightly improves the accuracy to a median error of $39\%$. Together, these results provide fitting functions for BCG stellar halos that can be applied to future wide-field surveys to infer halo masses from deep imaging data.

astro-ph.GA↗

arXiv2025

Cosmological Constraints from Dark Energy Survey Year 1 Cluster Lensing and Abundances with Simulation-based Forward-Modeling

We present a simulation-based forward-modeling framework for cosmological inference from optical galaxy-cluster samples, and apply it to the abundance and weak-lensing signals of DES-Y1 redMaPPer clusters. The model embeds cosmology-dependent optical selection using a counts-in-cylinders approach, while also accounting for cluster miscentering and baryonic feedback in lensing. Applied to DES-Y1, and assuming a flat $Λ$CDM cosmology, we obtain $Ω_m=0.254^{+0.026}_{-0.020}$ and $σ_8=0.826^{+0.030}_{-0.034}$, consistent with a broad suite of low-redshift structure measurements, including recent full-shape analyses, the DES/KiDS/HSC 3$\times$2 results, and most cluster-abundance studies. Our results are also consistent with \textit{Planck}, with the difference being significant at $2.58σ$. These results establish simulation-based forward-modeling of cluster abundances as a promising new tool for precision cosmology with Stage~IV survey data.

astro-ph.CO↗

arXiv2025

Testing the Stellar Feedback-driven Breathing Mode in Low-mass Galaxies with Gas Kinematics

Hydrodynamic simulations have proposed that stellar feedback and bursty star-formation can produce dark matter cores in low-mass galaxies. A key prediction is that feedback-driven gas outflow and inflow cycles can lead to ``breathing modes'' (rapid fluctuations in the global gravitational potential) which drive correlated variations in galaxy size, kinematics, and star-formation rate. In this paper, we test the dynamical effects of feedback-driven breathing modes using a sample of 103 star-forming low-mass galaxies with stellar masses between $7.9<\rm \log M_*/M_\odot<9.6$ and $0.02<z<0.19$. We measure ionized gas velocity dispersions from H$α$ emission lines and compare them to mock observations from the FIRE-2 simulations. We compare gas velocity dispersions ($\rm σ_{gas}$), stellar masses, and specific star-formation rates (sSFR). We find a positive correlation between gas velocity dispersion residuals at fixed stellar masses ($\rm Δσ_{gas}$) and sSFR in both data and simulations. However, the relation is tighter in FIRE-2 compared to the data. FIRE-2 produces more low-sSFR galaxies compared to our observational sample, however, the sSFR distributions agree after limiting both samples to a minimum sSFR. A deeper and more complete photometric sample further indicates that observed low-mass galaxies could span the full range of sSFR predicted in the FIRE-2 simulations. Our results support the existence of short-timescale dynamical effects driven by gas outflow and inflow cycles in low-mass galaxies and motivate additional tests of the breathing mode.

astro-ph.GA↗

arXiv2025

Impact of projection-induced optical selection bias on the weak lensing mass calibration of galaxy clusters

Weak gravitational lensing signals of optically identified clusters are impacted by a selection bias -- halo triaxiality and large-scale structure along the line of sight simultaneously boost the lensing signal and richness (the inferred number of galaxies associated with a cluster). As a result, a cluster sample selected by richness has a mean lensing signal higher than expected from its mean mass, and the inferred mass will be biased high. This selection bias is currently limiting the accuracy of cosmological parameters derived from optical clusters. In this paper, we quantify the bias in mass calibration due to this selection bias. Using two simulations, MiniUchuu and Cardinal, with different galaxy models and cluster finders, we find that the selection bias leads to an overestimation of lensing mass at the 20-50% level, with a larger bias (20-80%) for large-scale lensing (>3 Mpc). Even with a moderate projection model, this selection bias significantly outweighs other currently known cluster lensing systematics. This work confirms the need to account for this bias in future optical cluster cosmology analyses, and we discuss strategies for mitigating this bias.

astro-ph.CO↗

arXiv2025

User-Centric Communication Service Provision for Edge-Assisted Mobile Augmented Reality

Future 6G networks are envisioned to facilitate edge-assisted mobile augmented reality (MAR) via strengthening the collaboration between MAR devices and edge servers. In order to provide immersive user experiences, MAR devices must timely upload camera frames to an edge server for simultaneous localization and mapping (SLAM)-based device pose tracking. In this paper, to cope with user-specific and non-stationary uplink data traffic, we develop a digital twin (DT)-based approach for user-centric communication service provision for MAR. Specifically, to establish DTs for individual MAR devices, we first construct a data model customized for MAR that captures the intricate impact of the SLAM-based frame uploading mechanism on the user-specific data traffic pattern. We then define two DT operation functions that cooperatively enable adaptive switching between different data-driven models for capturing non-stationary data traffic. Leveraging the user-oriented data management introduced by DTs, we propose an algorithm for network resource management that ensures the timeliness of frame uploading and the robustness against inherent inaccuracies in data traffic modeling for individual MAR devices. Trace-driven simulation results demonstrate that the user-centric communication service provision achieves a 14.2% increase in meeting the camera frame uploading delay requirement in comparison with the slicing-based communication service provision widely used for 5G.

cs.NI↗

arXiv2025

The Outskirt Stellar Mass of Low-Redshift Massive Galaxies is an Excellent Halo Mass Proxy in Illustris/IllustrisTNG Simulations

Recent observations suggest that the extended stellar halos of low-redshift massive galaxies are tightly connected to the assembly of their dark matter halos. In this paper, we use the Illustris, IllustrisTNG100, and IllustrisTNG300 simulations to compare how different stellar aperture masses trace halo mass. For massive central galaxies ($M_\star\geq 10^{11.2}M_\odot$), we find that a 2D outskirt stellar mass measured between 50 to 100 kpc ($M_{\star,[50,100]}$) consistently outperforms other aperture-based stellar masses. We further show that $M_{\star,[50,100]}$ correlates better with halo mass than the total amount of accreted stars (the ex situ mass), which suggests that not all accreted stars connect to halo assembly equally. While the galaxy formation recipes are different between Illustris and IllustrisTNG100, the two simulations yield consistent ex situ outskirt fractions for massive galaxies (about 70% in $M_{\star,[50,100]}$). These results demonstrate the potential of using the outskirt stellar mass to deepen our understanding of galaxy-halo connection in massive dark matter halos and trace dark matter halos better.

astro-ph.GA↗

arXiv2025

QoE-Aware Service Provision for Mobile AR Rendering: An Agent-Driven Approach

Mobile augmented reality (MAR) is envisioned as a key immersive application in 6G, enabling virtual content rendering aligned with the physical environment through device pose estimation. In this paper, we propose a novel agent-driven communication service provisioning approach for edge-assisted MAR, aiming to reduce communication overhead between MAR devices and the edge server while ensuring the quality of experience (QoE). First, to address the inaccessibility of MAR application-specific information to the network controller, we establish a digital agent powered by large language models (LLMs) on behalf of the MAR service provider, bridging the data and function gap between the MAR service and network domains. Second, to cope with the user-dependent and dynamic nature of data traffic patterns for individual devices, we develop a user-level QoE modeling method that captures the relationship between communication resource demands and perceived user QoE, enabling personalized, agent-driven communication resource management. Trace-driven simulation results demonstrate that the proposed approach outperforms conventional LLM-based QoE-aware service provisioning methods in both user-level QoE modeling accuracy and communication resource efficiency.

cs.NI↗